Gilles Chabriel

Jean Barrère

Adaptive Target Detection Techniques for OFDM-Based Passive Radar Exploiting Spatial Diversity

Recently, it has been studied the benefit of using a channel-based detector (CHAD) with a passive radar exploiting OFDM waveforms radiated e.g. by DVB-T transmitters of opportunity.

When multiple antennas are available on receive, we state that CHAD can be seen as space-time array-processing performing on a particular coherent frequency datacube. Building on this new interpretation, we propose an improved version of CHAD in the form of a fully dimensional space-time adaptive processing. Optimization of the signal-to-interference-plus-noise ratio is obtained combining a linearly constrained minimum variance (LCMV) space-time adaptive beamforming and a least squares (LS) spatial adaptive filtering. Unlike classical STAP approaches, no training data are here used and only one space-time sample matrix inversion is required. The computational load is then highly reduced allowing a practical deployment. Moreover, since no beamscan in space is performed, the knowledge of array characteristics is not required and the performance shall not be impacted by any calibration errors. Finally, the full set of target signal returns being collected in a single range-Doppler surveillance map, the detection process is then simplified. Results on experimental data show the interests of this new surveillance scheme: no need for a prior rejection of the dominant interference, systematic reduction of secondary lobes, discrimination of slow moving targets.

Index Terms-passive bistatic radar (PBR), passive covert radar (PCR), passive coherent location (PCL), digital video broadcasting-terrestrial (DVB-T), orthogonal frequency division multiplexing (OFDM), spatial diversity, cross-ambiguity function (CAF), matched filtering, mismatched filter, reciprocal filter, clutter, linearly constrained minimum variance (LCMV) beamforming, CAPON's receptor, array-processing, space-time adaptive processing (STAP), least squares (LS) filtering.

I. INTRODUCTION

T his work addresses the problem of moving targets detection from a passive radar that exploits OFDMbased sources of illumination through a Rician fading channel. For such systems, a main issue arises from the presence of powerful propagation paths, mainly zero-Doppler paths as the line-of-sight (LOS) path and secondary paths due to relief, vegetation, buildings, etc., that may prevent detection of the targets of interest; the detection peaks corresponding to the weakest target signal returns being often buried by the clutter floor induced by these dominant paths.

Gilles Chabriel and Jean Barrère are with Aix Marseille Université, CNRS, Université de Toulon, IM2NP UMR 7334, CS 60584 -83041 Toulon Cedex 9, France (e-mail: gilles.chabriel@im2np.fr; jean.barrere@im2np.fr).

When the surveillance system involves multiple antennaelements (say R), space-time adaptive processing (STAP) theoretically offers a high-performing solution (see e.g. [START_REF] Ward | Space-time adaptive processing for airborne radar[END_REF], [START_REF] Tan | Signal Processing for Airborne Passive Radar : Interference Suppression and Space Time Adaptive Processing Techniques for Transmissions of Opportunity[END_REF] for airborne radars and [START_REF] Neyt | Feasibility of STAP for passive GSM-based radar[END_REF] for ground radars). STAP performs from a (R ⇥ M ⇥ K)-datacube obtained by cutting out each received signal r into M consecutive blocks (pulses) with K samples each. In order to assure temporal coherence within the datacude, the different pulses are classically matched filtered using the corresponding block of the reference signal i.e. the transmitted signal, assumed to be known. The space-time adaptive processor aims to exploit spatial diversity and thus to reach signal-to-interference-plusnoise ratio (SINR) optimization. It consists to implement for each range cell under test (one of the K (R ⇥ M )-slices of the datacube), a battery of space-time filters depending on Doppler shift and on at least one spatial parameter (a desired direction of arrival). For each Doppler shift and each direction, the optimal space-time adaptive filter is ideally built from the corresponding space-time steering vector and the inverse of the interference plus noise space-time covariance matrix. In the so-called sample matrix inversion (SMI) algorithm, this matrix is estimated averaging space-time snapshots (training data) taken in the vicinity of the under-test cell. Beside a high theoretical performance, SMI raises major practical issues. It is well known that a very important computational complexity is reached since for each range bin, a full dimension space-time covariance matrix has to be estimated and inverted. Moreover, the amount of available homogeneous training data is not always sufficient with respect to the number of degrees-of-freedom (DoF) brought by the space-time array. Hence, various DoF reduction strategies are used, conducting to suboptimal practical implementations and a dominant interference rejection prestage is often necessary before STAP. For ground radars, this dominant interference is mostly caused by zero-Doppler contribution (ZDC). Note that space-time approaches based on cross-ambiguity function [START_REF] Fabrizio | Adaptive beamforming for high-frequency over-the-horizon passive radar[END_REF], [START_REF] Zemmari | GSM Passive Radar for Medium Range Surveillance[END_REF], [START_REF] Villano | Antenna Array for Passive Radar: Configuration Design and Adaptive Approaches to Disturbance Cancellation[END_REF], [START_REF] Belfiori | Antenna Array Characterisation and Signal Processing for an FM Radio-Based Passive Coherent Location Radar System[END_REF], [START_REF] Alam | A multi-band multi-beam software-defined passive radar Part II: Signal processing[END_REF] also recommend a ZDC rejection. For airborne radars, rejection of the Doppler shifted strongest contributions is also required with the risk that some targets of interest (often in practice the slowest moving targets) fall in the rejection band of the filter [START_REF] Tan | Spacetime interference analysis and suppression for airborne passive radar using transmissions of opportunity[END_REF]. One can find e.g. in [START_REF] Poullin | Passive detection using digital broadcasters (DAB, DVB) with COFDM modulation[END_REF], [START_REF] Colone | A Multistage Processing Algorithm for Disturbance Removal and Target Detection in Passive Bistatic Radar[END_REF], [START_REF] Tao | Direct-path suppression by spatial filtering in digital television terrestrial broadcasting-based passive radar[END_REF], [START_REF] Chabriel | Passive Covert Radars using CP-OFDM signals: a new efficient method to extract targets echos[END_REF], different methods for such a ZDC rejection. It can however be highlighted that these preprocessings are in general sensitive to antenna impairments such as mechanical vibrations or RF front-end impairments such as carrier frequency offset (CFO) or sample frequency offset (SFO). So, one cannot expect to reach in practice a full ZDC rejection. A common point between all these approaches is the need to know the geometry of the (necessary calibrated) array so that beams in space can be scanned. In this paper and as in [START_REF] Pipon | Une approche "communication" pour la détection de cibles mobiles[END_REF] where a particular spatial extension of classical cross-ambiguity function (CAF) is proposed, we choose to address the problem of target detection in a blind way i.e. without any use of spatial (angular) parameterization. The classical spatial beamscan is then fully replaced by a minimization problem conducting directly to a unique (optimal) beam for each range-Doppler bin under test. Although the spatial information is lost or more accurately unused, such approaches have many advantages: target returns are always delivered in a single range-Doppler map, thereby simplifying the surveillance process by a human operator or an automatic tracker. Moreover, the performance can no longer be impacted by array calibration errors, as it can be with array-shape dependent methods. Finally and importantly, the computational load remains reasonably low when compared to those of classical STAP approaches. For OFDM-based emissions, it has been proposed in [START_REF] Searle | Evaluation of the ambiguity function for passive radar with OFDM transmissions[END_REF], [START_REF] Palmer | DVB-T Passive Radar Signal Processing[END_REF] to replace the battery of matched filters by "mismatched" filters (also called reciprocal filters). The idea is that reciprocal filter outputs being theoretically "signalindependent", presence of ghost targets (false alarms) caused by the DVB-T signal structure (cyclic prefix, transmission parameter signalling, pilot data, see [START_REF] Harms | Understanding the signal structure in DVB-T signals for passive radar detection[END_REF]) is naturally eliminated. In [START_REF] Gassier | A Unifying Approach for Disturbance Cancellation and Target Detection in Passive Radar using OFDM[END_REF], a new interpretation of such an approach was stated in term of channel-based detector (CHAD). Note that a similar interpretation can be found in [START_REF] Berger | Signal Processing for Passive Radar Using OFDM Waveforms[END_REF] for the particular case of DAB signals. It was demonstrated that, thanks to the orthogonality between the subcarriers, CHAD has, in general, less clutter leakages than using matched filters. In particular, the "low-Dopplerized" paths induce a near zero clutter floor level; this level theoretically reaching zero for ZDC. Here, we show that the multidimensional extension of CHAD can be seen as R classical data-independent (not adaptive) beamformers in time performing from a coherent frequency datacube where the number M of pulses corresponds to the number of OFDM symbols processed. From such an interpretation, we propose to replace the classical dataindependent beamformer by a linearly constrained minimum variance adaptive beamformer fully coupled with a least squares spatial adaptive filtering. We obtain a low complexity fully adaptive STAP free of any array spatial parameterization where the dominant interference prior rejection is no more required.

The paper is organized in the following way: Section II describes the spatial model on receive at baseband obtained under classical simplifying assumptions. The notion of Doppler channel is recalled and the theoretical expression of its frequency response is derived in the case of a DVB-T emission. In the section III, two multidimensional range-Doppler detection tools, namely space-time CAF and spacetime CHAD, are described. In Section IV we develop the proposed blind STAP (B-STAP) version of CHAD and we discuss its computational complexity. Examples of surveillance maps obtained with two experimental ground passive radars are presented in Section V where the performance of CAF (with and without ZDC rejection), of CHAD (with and without ZDC rejection) and finally of B-STAP CHAD are compared.

II. PROBLEM MODELING

A. Far-field spatial model

The schematic view in Fig. 1 illustrates a passive ground radar detection scenario where a transmitter of opportunity illuminates a scene composed of I 1 static or moving specular reflectors. A receive array-antenna composed of R elements collects the contributions of the transmitted signal from I propagation paths (the LOS path and I 1 secondary paths). Let us introduce as a radar reference signal, the LOS complex baseband signal received on an element indexed by 0, arbitrary chosen as a reference element. This signal denoted by s(t), matches the complex baseband transmitted signal up to a propagation delay t 0 due to LOS distance.

Throughout this paper, we denote generically by I I , the set of indices i = 0, . . . , I 1, that is to say I I = {0, 1, . . . , I 1}. Assuming validity of some classical simplifying assumptions (see the appendix for more details), the complex baseband signal y r (t) received on any sensor r 2 I R , is given by:

y r (t) = X i2I I ir s(t ⌧ i ) e |2⇡fit +⌘ r (t) (1)
where

• i > 0 is the index of a reflector. It varies from 1 to I 1 and i = 0 corresponds to the LOS path ; r is the index of an element of the receive array-antenna. It varies from 0 to R 1 (r = 0 being reserved for the reference element) ; a particular path is defined by the pair (i, r) ; • ⌧ i is the relative propagation delay such that ⌧ i + t 0 corresponds to the propagation delay of the path (i, 0) (i.e. from the transmitter to the reference sensor, via the i th reflector, see fig. 1), • f i is the Doppler frequency shift due to the i th reflector bistatic speed,

• ir are spatial complex factors depending on relative propagation small delays, radar cross-sections (RCS) of

B. Notion of Doppler channel

As introduced in [START_REF] Gassier | A Unifying Approach for Disturbance Cancellation and Target Detection in Passive Radar using OFDM[END_REF], the previous model (1) can be rewritten as a sum of F differently frequency shifted ("Dopplerized") contributions and one has using the convolution operator ⇤:

y r (t) = X j2I F h fj r ⇤ s (t) e |2⇡fj t +⌘ r (t),
where f j (j 2 I F ) is one of the distinct Doppler frequency shifts, and where h fj r (t) represents the impulse response of the linear filter that models the multipath between the source and the receiver r at Doppler shift f j . Such a filter characterizes the so-called Doppler channel associated with the element r and corresponding to the Doppler frequency shift f j . Introducing the index subset J fj of I I , corresponding to a given Doppler frequency shift f j such as

J fj = {i 2 I I /f i = f j } , we have from (1), 8j 2 I F , 8r 2 I R h fj r (t) = X i2J f j ir (t ⌧ i ),
where (t) represents the Dirac pseudo-function. Presence of a propagation path i with a Doppler shift f j and a bistatic delay ⌧ i is then betrayed by a pulse in the impulse response h fj r (t). Equivalently, if F{.} denotes the Fourier transform, the contribution of such a path ideally takes the form of a complex exponential in the corresponding frequency response H fj r (⌫) , F{h fj r (t)}.

C. The DVB-T particular case

For DVB-T illuminators (see [20]), the digital reference signal is a sequence of so-called CP-OFDM symbols s m [n], N samples each, such that , are obtained by a periodic extension of the useful part. In order to mitigate crosstalk issues between consecutive transmission channels, the lowest and highest frequency subcarriers have a zero amplitude and one can equivalently say that they are modulated by zero data. Hence, we denote by E K , the index subset of non-zero data:

s m [n] = 1 K X k2I K d k m e |2⇡ kn
E K = k 2 I K /d k m 6 = 0 .
As most of the authors on this topic, we assume here that all the required conditions for an error-free DVB-T demodulation are fulfilled [START_REF] Gassier | Pilot-Aided Beamforming for OFDM Detection Over Severe Multipath Fading[END_REF]. As a consequence, the sampled version y r [n] (sample period: T u /K) of the received signal is assumed to be synchronized to the begining of the useful part of the LOS dominant contribution and the whole dataset

d k m k2E K m2I M is considered known. The digital reference signal s m [n] n2I K m2I M
can then be resynthesized by IDFT and periodic extension without any error.

Assuming that ⌧ i < T cp , 8i 2 I I , each sample a in the useful part of a received block m on an element r yields

y mr [n] = X j2I F h 1 K X k2E K H fj r [k]d k m e 2|⇡ kn K i e |2⇡fj (mTs+n T u K ) + ⌘ mr [n], n 2 I K , (2)
with T s , T u + T cp and where

H fj r [k] = X i2J f j ir e 2|⇡ k⌧ i Tu , k 2 E K (3)
is the frequency response of the Doppler channel associated with the Doppler frequency shift f i . The samples ⌘ mr [n] correpond to the additive noise contribution.

For sufficiently low Doppler shifts, it can be assumed that the phase rotation e |2⇡fit in (1) (or equivalently e |2⇡fj (mTs+n T u K ) in ( 2)) remains constant within each CP-OFDM block of duration T s . Under this assumption, one can replace the phasor e |2⇡fj (mTs+n T u K ) by e |2⇡fj mTs in the expression [START_REF] Tan | Signal Processing for Airborne Passive Radar : Interference Suppression and Space Time Adaptive Processing Techniques for Transmissions of Opportunity[END_REF]. It comes

y mr [n] = X j2I F h 1 K X k2E K H fj mr [k]d k m e 2|⇡ kn K i + ⌘ mr [n],
where

H fj mr [k] = X i2J f j ir e |2⇡fj mTs e 2|⇡ k⌧ i Tu , k 2 E K (4)
corresponds to the instantaneous frequency response of the f j -Doppler channel varying from symbol to symbol.

III. MULTISENSOR RANGE-DOPPLER DETECTION TOOLS

In this section, we give the expressions of two range-Doppler detection multidimensional (spatial) tools used in passive radar (CAF and CHAD) in which the knowledge of the array shape is not required. a some of the samples for Tcp < ⌧ i < Tcp + Tu Classical CAF is built from cross-correlations between the received signal y mr [n] n2I K m2I M on an element r of the array and a bank of differently delayed (⌧ n 0 ), differently frequency shifted (f ) versions of the power-normalized reference sequence

s m [n] n2I K m2I M : X r [n 0 , f] = 1 KM X n2I K m2I M y mr [n](s m [n n 0 ]) ⇤ e 2|⇡f mTs ,
with ⌧ n 0 = n 0 Tu K . Then, CAF expression is given by the square modulus of X r [n 0 , f]:

A r [n 0 , f] = |X r [n 0 , f]| 2 .
A multidimensional (spatial), blind (not requiring any spatial model like steering vector), extension of CAF can be found in [START_REF] Pipon | Une approche "communication" pour la détection de cibles mobiles[END_REF]. This approach consists in identifying the weight vector w = (w r ) r2I R of the least squares (LS) spatial filter minimizing the following mean square error:

" 2 [n 0 , f] = 1 MK X n2I K m2I M w H y m [n] s m [n n 0 ] e 2|⇡f mTs 2
where y m [n] is the column vector of size R gathering each n th sample of each m th pulse, received by the R antennas:

y m [n] = (y mr [n]) r2I R .
Minimization of " 2 yields:

w[n 0 , f] = 1 y X[n 0 , f],
where the R ⇥ R matrix

y , 1 KM X n2I K X m2I M y m [n]y H m [n]
is an estimate of the spatial variance covariance matrix of the different recordings, and where

X[n 0 , f] = (X r [n 0 , f]) r2I R .
The blind space-time CAF is finally given by the energy of the spatially filtered observations

A ST [n 0 , f] = X H [n 0 , f] 1 y X[n 0 , f]. (5)
It can be demonstrated that (5) gives the optimal solution, in the maximum likelihood ratio sense, when the problem would be to detect a single propagation path observed through multiple independent AWGN channels. Note that the outputs of this particular CAF remain in the range-Doppler plane unlike other approaches (see e.g. [START_REF] Fabrizio | Adaptive beamforming for high-frequency over-the-horizon passive radar[END_REF], [START_REF] Zemmari | GSM Passive Radar for Medium Range Surveillance[END_REF], [START_REF] Villano | Antenna Array for Passive Radar: Configuration Design and Adaptive Approaches to Disturbance Cancellation[END_REF], [START_REF] Belfiori | Antenna Array Characterisation and Signal Processing for an FM Radio-Based Passive Coherent Location Radar System[END_REF], [START_REF] Alam | A multi-band multi-beam software-defined passive radar Part II: Signal processing[END_REF]) using CAF built on a spatial model where the angular parameters increase the output dimension.

B. Doppler channel detector (CHAD)

Let us denote by Y f mr [k] the spectral representation of the so-called Doppler compensated sequence of samples y f mr [n] = y mr [n] e 2|⇡f mTs obtained using a K-points DFT:

Y f mr [k] = X n2I K y mr [n] e 2|⇡f mTs e 2|⇡ kn K . ( 6
)
From ( 2), an estimation of the instantaneous frequency response Ĥf mr [k] of the f -Doppler channel is then given 8k 2 E K , by

Ĥf mr [k] = Y f mr [k] d k m (7)
and for the zero-amplitude subcarriers i.e. 8k 2

I K \E K , by Ĥf mr [k] = 0.
It has been shown in [START_REF] Gassier | A Unifying Approach for Disturbance Cancellation and Target Detection in Passive Radar using OFDM[END_REF] that when the number of symbols M increases, the following summation

Ĥf r [k] , 1 M X m2I M Ĥf mr [k], k 2 E K , (8)
asymptotically converges to the frequency response H f r [k] of the Doppler channel associated to the frequency shift f . Then, the square modulus of the corresponding impulse response

h f r [n 0 ] obtained by a K-points IDFT: ĥf r [n 0 ] = 1 K X k2E K Ĥf r [k] e 2|⇡ kn 0 K , (9)
leads to the Doppler channel detector CHAD:

C r [n 0 , f] = | ĥf r [n 0 ]| 2 ,
where the presence of a propagation path with a Doppler shift f and a sample shift n 0 is ideally betrayed by a Dirac pulse.

As for CAF, we propose here a multidimensional (still blind) extension of CHAD searching the LS spatial filter w = (w r ) r2I R that minimizes the following quadratic error

" 2 [n 0 , f] = X k2E K w H H f [k] e 2|⇡ kn 0 K 2 , ( 10
)
where

H f [k] is the column vector of size R such that H f [k] = ( Ĥf r [k]) r2I R
and where e 2|⇡ kn 0 K represents (up to the unknown complex factor ir ) the ideal contribution of a path with a sample shift n 0 in the Doppler channel frequency response associated to the Doppler shift f (see [START_REF] Neyt | Feasibility of STAP for passive GSM-based radar[END_REF] replacing

⌧ i by n 0 Tu K ).
The spatial filter w optimizing " 2 in ( 10) is given by

w[n 0 , f] = 1 H f h f [n 0 ] where the R ⇥ R matrix H f , 1 K X k2I K H f [k](H f [k]) H
is an estimate of the spatial covariance matrix of the different frequency responses, and where h f

[n 0 ] = ( ĥf r [n 0 ]) r2I R . The output C ST [n 0 ,
f] of the space-time CHAD is finally obtained computing the energy of the spatially filtered Doppler channel

C ST [n 0 , f] = (h f [n 0 ]) H 1 H f h f [n 0 ]. ( 11
)
Radar relative bistatic range limitation: for simplicity the previous theoretical developments have been written considering relative propagation delays ⌧ i less than the guard interval duration T cp . It corresponds to a 70km relative bistatic coverage for the largest guard interval duration in European DVB-T emissions and to 8.75km for the shortest one. Now, it has been stated in [START_REF] Gassier | A Unifying Approach for Disturbance Cancellation and Target Detection in Passive Radar using OFDM[END_REF] that this limitation can reasonably be overridden in return for a gradual detection loss due to the proportion of samples outside the guard interval. Note that this is the case for the experimental results at the end of this paper where relative bistatic delays up to three time the guard interval duration are explored. Moreover this only represents a relative coverage and the LOS distance must be added to obtain the absolute coverage.

IV. PROPOSED B-STAP DETECTOR

A. CHAD frequency datacube

Observing ( 4), it appears that, for a given array-element r, the M instantaneous frequency responses H fj mr [k] k2E K can be seen as snapshots of a sinusoidal plane wave impinging a M -elements temporal uniform linear array (ULA). The wave being "spatially" characterized by its Doppler shift f j . This array-processing analogy can be extended when all the R antennas of the receive array are considered to form a set of R temporal ULAs with M elements each. In that case, the unknown coefficients ir affect the MR-elements antenna global manifold. Now, using ( 6) and [START_REF] Belfiori | Antenna Array Characterisation and Signal Processing for an FM Radio-Based Passive Coherent Location Radar System[END_REF], the expression of the estimated f -Doppler channel (8) can be rewritten as

Ĥf r [k] = 1 M X m2I M Ĥmr [k] e 2|⇡f mTs , k 2 E K ,
where the instantaneous frequency responses ( Ĥmr [k]) k2E K are obtained using [START_REF] Belfiori | Antenna Array Characterisation and Signal Processing for an FM Radio-Based Passive Coherent Location Radar System[END_REF] with no Doppler compensation (i.e. by taking f = 0). This set of MRK samples Ĥmr [k] forms the frequency datacube used by CHAD. For each element r, the CHAD processor can then be interpreted as a conventional phaseand-sum beamformer performing on the associated temporal ULA r. It aims to enhance the selected f -Doppler channel by focusing towards its Doppler direction.

Building on this reinterpretation of CHAD, we propose two main improvements. A first improvement concerns the choice of the beamformer associated to the R temporal ULAs. CHAD uses a classical data-independent beamformer which only maximizes the signal to noise ratio (SNR). In order to maximize SINR, we then propose to replace the classical beamformer by a linearly constrained minimum variance (LCMV) adaptive (data-dependent) beamformer.

A second improvement concerns the way that the R temporal ULAs are used. With CHAD, the Doppler beamformer does not exploit the spatial diversity brought by the coefficients ir . Time and space dimensions are indeed processed independently: the time dimension only concerns the temporal beamforming while the space dimension appears with the LS spatial filtering. To improve the performance, we then propose to jointly use the temporal and spatial elements to build a space-time adaptive filter.

B. Space-time adaptive SINR maximization

Let us assume the potential presence of a propagation path with the bistatic delay ⌧ n 0 = n 0 T u /K and the Doppler shift f . For each array-element r and each received pulse m, its theoretical contribution to the instantaneous frequency response of the f -Doppler channel can be modeled using ( 4) by

H f mr [k] = r e |2⇡f mTs e 2|⇡ kn 0 K , k 2 E K .
Referring again to the notion of temporal ULA, the sequence of space-time coefficients r e |2⇡f mTs r2I R m2I M can be seen as a space-time array manifold. The M coefficients u f m , e |2⇡f mTs characterize the Doppler direction of the sinusoidal plane wave e 2|⇡ kn 0 K while the R unknown coefficients r correspond to its spatial direction.

Let us consider now the MRK samples of the frequency datacube Ĥmr [k]. By linearly combining them, one defines a space-time filtered frequency response:

Z[k] = X m2I M X r2I R w ⇤ mr Ĥmr [k]
where w mr , m 2 I M , r 2 I R are the MR coefficients of the space-time filter. Using array-processing vocabulary, our SINR maximization objective can be formulated as follows: steering to the Doppler direction f , the output samples Z[k] have to "fit at best" the theoretical samples s n 0 [k] , e 2|⇡ kn 0 K characterizing the expected plane wave while minimizing the energy of contributions coming with other Doppler directions. That multi-objective problem shall be achieved jointly solving two coupled adaptive optimization schemes: a problem of linearly constrained minimum variance (LCMV) space-time adaptive beamforming combined to a problem of LS spatial adaptive filtering.

1) LCMV adaptive beamformer: the energy e(w) at filter output is given by

e(w) = 1 K X k2I K Z[k]Z ⇤ [k] = 1 K X m,m 0 ,r,r 0 w ⇤ mr w m 0 r 0 X k Ĥmr [k]( Ĥm 0 r 0 [k]) ⇤ . ( 12
)
For a plane wave with a Doppler direction f , the complex magnitude gain g f r brought by the temporal part (w mr ) m2I M of the filter associated to the temporal ULA r yields

g f r = X m2I M (w mr ) ⇤ u f m , 8r 2 I R . ( 13
)
The LCMV formulation of our problem is then to find the optimal coefficients (w opt mr )m2I M r2I R minimizing ( 12) with respect to the R linear temporal steering constraints [START_REF] Chabriel | Passive Covert Radars using CP-OFDM signals: a new efficient method to extract targets echos[END_REF]. The necessary knowledge of the gains g f r is here relaxed; they will be determined later during the LS filtering process. One can then write the following constrained optimization problem

(w opt mr )m2I M r2I R = argmin (wmr)mr n 1 K X m,m 0 ,r,r 0 w ⇤ mr w m 0 r 0 X k Ĥmr [k] Ĥ⇤ m 0 r 0 [k] + X r2I R µ r ⇣ g f r X m2I M w ⇤ mr u f m ⌘o , (14)
where the coefficients µ r are the R Lagrange multipliers associated to the R constraints.

Using the column stacking operator vec[.], the previous equation can be rewritten in matrix-vector notation. We then define the following column vectors and matrix:

the MR-dimensional vector H[k] = vec h⇣ Ĥmr [k] ⌘ m2I M r2I R i
gathering the instantaneous frequency responses, the MR-

vector w = vec h (w m,r )m2I M r2I R
i of the filter coefficients, the R-vector of the complex gains

g f = g f r r2I R , the MR ⇥ R matrix of constraints C f = 2 6 4 u f 0 . . . . . . . . . 0 u f 3 7 5
where

u f = u f m m2I M
is the M dimensional steering vector corresponding to the Doppler direction f and finally µ f = µ f r r2I R , the vector of the R Lagrange multipliers. With these notations, [START_REF] Pipon | Une approche "communication" pour la détection de cibles mobiles[END_REF] becomes

w opt = argmin w n w H w + g H f w H C f µ f o , (15)
where

= 1 K X k H[k]H H [k],
is an estimate of the Hermitian (MR ⇥ MR) space-time covariance matrix. Nulling the gradient of the Lagrangian in [START_REF] Searle | Evaluation of the ambiguity function for passive radar with OFDM transmissions[END_REF] with respect to (w.r.t.) w H , it comes

w opt = 1 C f µ f . ( 16
)
Now, replacing the expression (16) of w in the linear system of constraints [START_REF] Chabriel | Passive Covert Radars using CP-OFDM signals: a new efficient method to extract targets echos[END_REF], g H f = w H C f , we get the Lagrange multiplier vector:

µ f = C H f 1 C f 1 g f .
The general expression of the space-time filter ( 16) w.r.t. the unknown spatial complex gains then yields

w opt = Mg f , ( 17
)
where M is the following (MR ⇥ R) rectangular matrix

M = 1 C f C H f 1 C f 1 .
2) LS spatial adaptive filtering: we can now set the optimal complex gain vector g opt so that the samples Z[k] fit those of the sinusoidal plane wave s n 0

[k] = e 2|⇡ kn 0 K in the LS sense:

w opt = argmin w ( 1 K X k2I K w H H[k] s n 0 [k] 2 ) . ( 18
)
Replacing w using ( 17), an equivalent formulation is

g opt = argmin g f X k2I K 1 K g H f M H H[k] s n 0 [k] 2 = argmin g f n g H f M H Mg f g H f M H 1 K X k2I K (H[k]s ⇤ n 0 [k]) 1 K X k2I K (s n 0 [k]H H [k])Mg f + 1 K X k2I K |s n 0 [k]| 2 o .
Nulling the gradient of the mean square error w.r.t. g H f , we get

g opt = M H M 1 M H 1 K X k2I K (H[k]s ⇤ n 0 [k]) . ( 19
)
Replacing the previous expression of g opt in [START_REF] Harms | Understanding the signal structure in DVB-T signals for passive radar detection[END_REF], it comes

w opt = M M H M 1 M H 1 K X k2I K (H[k]s ⇤ n 0 [k]) . (20) Now, since s ⇤ n 0 [k] = e 2|⇡ kn 0
K , it appears that the expression

1 K P k2I K H[k]s ⇤ n 0 [k] stands for the IDFT of H[k] k2I K , denoted by h[n 0
] n 0 2I K . Finally, using [START_REF] Gassier | A Unifying Approach for Disturbance Cancellation and Target Detection in Passive Radar using OFDM[END_REF], we get

w opt [n 0 , f] = 1 C f C H f 1 C f 1 C H f 1 h[n 0 ]. (
C B-STAP [n 0 , f] = h H [n 0 ] 1 C f C H f 1 C f 1 C H f 1 h[n 0 ]. (22)

C. Computational load

Pilot-aided synchronization and OFDM symbol detection are two essential prior steps for all the methods to provide an ideal noise-free reference signal. The computational load of these two steps remains very low since it simply corresponds to a classical DVB-T decoding: synchronization by correlation with the pilot sequence can be performed only once using a few symbols. Then, extraction of the sequence of M OFDM symbols using K-points FFT leads to a linear complexity:

O (MK log 2 K).
Let us put it in the extreme case where one wants to scan K bistatic delays ⌧ n 0 and then to compute, for a given Doppler shift f , the (K)-vector (C STAP [n 0 , f])) n 0 2I K . In the expression [START_REF] Poullin | Elevation angle estimation for low-altitude targets using DVB (SFN broadcasters)[END_REF], the K (MR)-vectors ). Note that K space-time matrices should have to be inverted using SMI algorithm, increasing the computational load to O(KM 3 R 3

).

For each value of the Doppler frequency shift f , one first has to compute the

K (R)-vectors (C H f 1 h[n 0 ]) n 0 2I K (MR 2 K complex operations) and the (R ⇥ R)-matrix C H f 1 C f 1 (2M 2 R 3
+ R 3 complex operations). Finally, K(R 2 + R) complex operations are still needed to complete calculation of the (K)-vector (C STAP [n 0 , f])) n 0 2I K . The total number of operations is then

MR 2 K + 2M 2 R 3 + R 3 + K(R 2 + R) i.e. a O(MR 2 K) computational complexity .
By contrast, efficiently computing the cross-ambiguity function in the frequency domain, using the FFT, one obtains the complexity O(MRK log 2 K) for each frequency shift f . Thus, with e.g. K = 8192 orthogonal subcarriers, the proposed detector (B-STAP CHAD) has a lower computational cost than CAF if the number R of antennas is less than log 2 (K) = log 2 (8192) = 13.

V. TARGET RETURNS FROM EXPERIMENTAL DATA

We dispose of two experimental datasets delivered by two prototypes of passive ground radar. The first dataset referred as Dataset #1, is provided by a 4-elements ULA. The target of interest is an helicopter illuminated by the French DVB-T transmitter from the Eiffel tower during the french Bastille day. The second dataset (Dataset #2) comes from a second prototype using a 16-elements 2D planar receive array. One can learn more about this second system by reading [START_REF] Poullin | Elevation angle estimation for low-altitude targets using DVB (SFN broadcasters)[END_REF], [START_REF] Poullin | Passive 3D tracking of low altitude targets using DVB (SFN Broadcasters))[END_REF]. The objective is here to discriminate a fighter aircraft mainly illuminated by three German DVB-T transmitters using the same HF carrier frequency (single frequency network, SFN).

A. Dataset #1 (helicopter -4-elements ULA):

The characteristics of the DVB-T emission are 8K (K = 8192 orthogonal subcarriers), 64-QAM and T cp = T u /32. All detection results are obtained processing the same sequence of M = 190 consecutive OFDM symbols (pulses) corresponding to an observation duration of approximatively 170ms. The reference signal is extracted from a dedicated DVB-T directive antenna pointing towards the Eiffel tower. In the sequel, relative bistatic distances up to 25km are scanned corresponding to 3 times the distance covered by the wave during the guard interval duration. In the range-Doppler plane, the theoretical location of the target echo of interest (17.1km,-173.0Hz) is marked by a black circle. Such a range value corresponds to a relative bistatic delay more than twice as large as the CP duration and a slight discrimination loss (about 10 log 10 (( 322) 2 /32 2 ) = 0.57dB, see [START_REF] Gassier | A Unifying Approach for Disturbance Cancellation and Target Detection in Passive Radar using OFDM[END_REF]) is expected using CHAD with respect to CAF. To tackle dynamic range issues between the different target returns, we adjusted, in each following 3D surveillance map, the colorscale so that the highest peak with a unit normalized level, appears with a black color while the clutter floor has always the same gray/blue color. In that way, whatever the process used, a detection peak is equally (and fairly) visible depending on its relative level wrt the clutter floor.

The figure 2 shows first target returns obtained using CAF (5) without rejecting the zero-Doppler contribution. Except for the dominant LOS signal and for some other zero-Doppler returns, no moving target is visible. They are all masked by the clutter floor high level mainly induced by the LOS path.

The next result (Fig. 3) is obtained using space-time CHAD [START_REF] Colone | A Multistage Processing Algorithm for Disturbance Removal and Target Detection in Passive Bistatic Radar[END_REF] without ZDC rejection. The clutter floor level being here the clutter level reduction brought by CHAD reaches about 20dB, allowing to bare the helicopter return. Fig. 5 corresponds to target returns from CAF implementing the ZDC rejection frequency method proposed in [START_REF] Chabriel | Passive Covert Radars using CP-OFDM signals: a new efficient method to extract targets echos[END_REF]. As expected, one can see that fully ZDC rejection is not reached and quite important secondary lobes remain along the Doppler axis in particular for low bistatic ranges. This could be caused by RF impairments (residual CFO) or mechanical impairments (low frequency vibrations of the antenna support). The ZDC rejection level is nevertheless sufficient to unmask several target returns including that of interest within the black circle.

A similar result (see Fig. 6) is obtained using space-time CHAD implementing the embedded ZDC rejection method proposed in [START_REF] Chabriel | Passive Covert Radars using CP-OFDM signals: a new efficient method to extract targets echos[END_REF]. The range values (from 15km to 19km, i.e. around the target-of-interest location) in Fig. 7 show that after ZDC rejection, the two detectors exhibit close clutter relative levels. The following result (see Fig. 8) is obtained using the proposed B-STAP detector C B-STAP [START_REF] Poullin | Elevation angle estimation for low-altitude targets using DVB (SFN broadcasters)[END_REF]. One can see, which shows the interest of this new approach, that moving targets including that of interest (black circle) are now clearly visible without the need for a ZDC rejection process. It can be observed an important systematic reduction of the secondary lobes. In particular, the ZDC secondary lobes near the null bistatic range axis have been drastically removed thanks to the SINR maximization.

As previously, one can see from the range values in Fig. 9, that similar clutter relative local levels are obtained both with A ST (after ZDC rejection) and C B-STAP .

B. Dataset #2 (fighter aircraft -16-elements 2D array):

Here, the characteristics of the DVB-T emission are 8K, 16-QAM, T cp = T u /4. M = 82 OFDM symbols (pulses) are processed for target discrimination. Its corresponds to a coherent integration time of about 83ms. The reference signal is obtained from the radar receive array-antenna using the spatial approach proposed in [START_REF] Gassier | Pilot-Aided Beamforming for OFDM Detection Over Severe Multipath Fading[END_REF]. The 16 recordings are synchronized to the LOS path of the nearest transmitter. A bistatic coverage up to about 140km is explored. It corresponds to bistatic delays up to 2 times the guard interval duration. The target of interest (the fighter aircraft) being illuminated by three DVB-T transmitters (SFN configuration), three returns are expected in the range-Doppler plane with the following coordinates: (8.1km,-338Hz), (33.8km,-249Hz) and (76.8km,-123Hz). These theoretical locations will be marked by circles in the surveillance maps. Note that the aircraft echo with the highest range coordinate (76.8km) falls outside the guard interval.

A first surveillance map is presented in Fig. 10. It is obtained using CAF without implementing the rejection of the dominant zero-Doppler contribution. One can observe several zero-Doppler powerful returns corresponding to the three LOS signals from the three SFN transmitters and to several strong replicas. In addition, one can see a series of aligned plots with Doppler shifts of about -240Hz. They are false alarms (ghost returns) due to the particular structure of the DVB-T signal [START_REF] Harms | Understanding the signal structure in DVB-T signals for passive radar detection[END_REF]. The target-of-interest returns remain masked by the clutter floor induced by the dominant contribution.

The next figure presents a surveillance map obtained using the space-time extension of CHAD without ZDC rejection. As with the first dataset, one observes a very important performance than with CAF (see previous Fig. 12) is observed, except that the ghost returns are now eliminated.

The following result (Fig. 14) is obtained using the proposed detector C B-STAP . As a result of SINR maximization, the three LOS return peaks are now correctly relocated around the zero-Doppler axis hence the surveillance map is globally "cleaner" than that obtained using the space-time detector CAF or even using CHAD. The three aircraft returns are perfectly discriminated wrt the background. In addition, since no ZDC rejection was performed, several slow moving targets are now found. Most of them are due to the presence of a civil airport and an highway within the radar range [START_REF] Poullin | Elevation angle estimation for low-altitude targets using DVB (SFN broadcasters)[END_REF], [START_REF] Poullin | Passive 3D tracking of low altitude targets using DVB (SFN Broadcasters))[END_REF]. peak of interest, a similar performance is reached by the two processings. One can however observe the ability of B-STAP CHAD to remove secondary lobes; in the figure, a secondary lobe coming from one of the three LOS propagation paths with a relative bistatic distance of about 54.8km. The last two figures Fig. 16 and Fig. 17 present range-Doppler trackings of one of the three aircraft returns (that illuminated by the nearest DVB-T transmitter). For each method (space-time CAF with ZDC rejection and blind spacetime adaptive CHAD), 12 range-Doppler surveillance maps have been computed from 12 sequences of 82 OFDM pulses each. The sequences being separated by 1 second, we obtain a 12 seconds tracking. These maps were combined (elementwise maximum) then thresholded to produce 2D binary detection maps. All the 12 returns forming a "Z"-shaped track are detected by the two approaches with a similar performance. In addition to the ghost returns suppression, the main difference lies in the ability of the proposed method to work with the dominant interference while with CAF, this disturbance must first be removed.

VI. CONCLUSION

In this paper, a new fully adaptive STAP for range-Doppler detection of targets with ODFM-based passive radars is developed. Unlike classical STAP approaches that perform from matched filtered datacubes, the proposed method uses a channel-based (signal-independent) frequency datacube built on the particular structure of the OFDM pulses. Considering a set of complex exponentials as a new target signal model to be detected in the CHAD datacube, we show that SINR maximization can be reached for the whole set of range-Doppler bins, from a unique space-time sample matrix without using any training data. This explains the very low computational cost of the method. Additionally, since no spatial beamscan is performed, the knowledge of the array shape is not required and the target detection is processed from a single range-Doppler map. Its use on experimental datasets illustrates the practical interests of such an approach: dominant interference prerejection not required, secondary lobes reduction, ghost returns elimination, detection improvement for weak slow moving targets. The proposed approach dealing intrinsically with a wide range of hardware impairments, it allows to easily implement a performing passive radar system for a low cost. Finally, B-STAP CHAD should be useful for OFDM-based airborne passive radars where the presence of the (generally Dopplerized) dominant interference would no longer be a major issue.

ACKNOWLEDGMENTS

The campaign of measurements associated to Dataset #2 was achieved by Thales Air Systems, ONERA, DGA (Direction genérale de l'armement) and CEAM (Centre d'Experiences Aériennes Militaires) jointly with BWB (Bundesamt fur Wehrtechnik und Beschaffung) and the Fraunhofer Institute. We are particularly grateful to J. Colin, from DGA, for giving us access to data.

APPENDIX Receive Signal Modeling -Simplifying Assumptions

The LOS RF complex signal received by the reference antenna-element (indexed by 0) is generically denoted by: s(t) = s(t)e 2|⇡F0t , where s(t) represents the complex envelop (baseband signal) and F 0 the HF carrier frequency. Let us assume that it exists at the bistatic initial distance d(i, 0), a moving reflector i with the constant bistatic speed v i . The corresponding reflected signal impinges the reference element with a LOS relative varying delay ⌧ 0 i (t). Its contribution to the reference element is then proportional to s t ⌧ 0 i (t) = s t ⌧ 0 i (t) e 2|⇡F0(t ⌧ 0 i (t)) .

Assume also that the bistatic speed v i is sufficiently low so that the distance covered by the target during the observation time T is negligible with respect to the bistatic distance d(i, 0):

v i T ⌧ d(i, 0).
This allows to limit the degree of the polynomial expression ⌧ 0 i (t) to one and we then have ⌧ 0 i (t) ⇡ di c + vi c t, t 2 [0, T ], where d i = d(i, 0) d(0, 0) is the bistatic distance relative to the LOS distance and where c is the light speed. Still assume that the distance covered by the target during the observation time T is inferior to the shortest wavelength in baseband:

v i T ⌧ c B ,
where B is the bandwidth of the signal s(t). One can then ignore the effect of the first order term vi c t within the complex envelop and write s t ⌧ 0 i (t) ⇡ ↵ i s(t ⌧ i ) e 2|⇡fit e 2|⇡F0t , t 2 [0, T ], where ⌧ i = di c , ↵ i = e 2|⇡F0⌧i and where f i = F 0 vi c is the Doppler frequency shift.

On any antenna-element r 6 = 0, the signal is received with an algebraic relative delay denoted by ⌧ 0 ir (t). Due to the small dimension of the array-antenna with respect to the bistatic distances involved, it is reasonable to assume that the speed v i is viewed in the same way by all the elements and this relative delay can be considered as a constant (⌧ 0 ir (t) ⇡ ⌧ ir ) during the observation time. Additionally, the dimension of the array being very small compared to the shortest wavelength in baseband, one can assume that B max r [⌧ ir ] ⌧ 1 and consequently the effect of ⌧ ir within the complex envelop can be neglected. It follows that 8t 2 [0, T ], the contribution of the reflected signal that impinges the array-element r yields ↵ i e 2|⇡F0⌧ir s(t ⌧ i ) e 2|⇡fit e 2|⇡F0t .

Finally, considering now a general multipath propagation scheme, the whole baseband signal y r (t) received on any element r can then be modeled by a noisy linear combination of the IQ frequency downconverted contributions [START_REF] Poullin | Passive 3D tracking of low altitude targets using DVB (SFN Broadcasters))[END_REF]:

y r (t) = X i2I I ir s(t ⌧ i ) e |2⇡fit +⌘ r (t), t 2 [0, T ],
where the noise term ⌘ r (t) is assumed to be white, Gaussian and statically independent from one element r to another.

In practice, the complex coefficients ir do not exactly match ↵ i e 2|⇡F0⌧ir since they may include all additional effects modifying the phases and the amplitudes of the different contributions. These effects depend on target radar crosssection, on the complex gain spatial pattern of each antennaelement r, and on other unknown impairments such as antenna calibration errors.

Fig. 1 .

 1 Fig. 1. Far-field modeling.

  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSP.2017.2742980, IEEE Transactions on Signal Processing the different reflectors and eventually on some impairments about the array-antenna such as e.g. the spatial gain pattern of each element. The coefficients ir i2I I r2I R are generally distinct; they express the spatial diversity brought by the array-antenna. • ⌘ r (t) is the contribution of the additive white Gaussian noise (AWGN).

K n 2 [

 2 N + K, . . . , 0, . . . , K 1], m 2 I M , where K is the number of orthogonal subcarriers with the frequency spacing 1/T u . I K defines the support of the useful part whose duration is T u and [ N + K, . . . , 1] defines the support of the cyclic prefix (CP) with a T cp duration. Finally, d k m is the complex information datum that modulates the subcarrier k Tu during the emission of the symbol m. In practice, the useful part of the symbol m, i.e. (s m [n]) n2I K , is built from the inverse discrete Fourier transform (IDFT) of the discrete sequence of data (d k m ) k2I K , and the samples in the CP part (s m [n]) n2[ N +K,..., 1]

  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSP.2017.2742980, IEEE Transactions on Signal Processing A. A "blind" cross-ambiguity function (CAF)

21 )

 21 The corresponding space-time adaptive detector C B-STAP [n 0 , f] is finally built computing the energy 1 K P k |Z[k]| 2 at the filter output. It straightforwardly comes using Z[k] = (w opt ) H H[k] with (21):

  1 h[n 0 ] do not depend on the Doppler frequency shift under test f and are then calculated only once. Since a K-points fast Fourier transform (FFT) can be used to efficiently compute h[n 0 ] from H[k], the computational load remains dominated by the inversion of the (MR ⇥ MR) matrix and one can then expect a computational complexity bounded by O(M 3 R 3

Fig. 2 .

 2 Fig. 2. Space-time CAF A ST (dB) without ZDC rejection.

Fig. 3 .

 3 Fig. 3. Space-time CHAD C ST (dB) without ZDC rejection. lower than using CAF, more important ZDC leakages appear in the detection map. These secondary lobes nevertheless do not mask the target of interest which is now visible inside the black circle. A cut along the Doppler shift of the target of interest (-173 Hz) is presented in the upper-bound normalized plot of Fig. 4 for each of the two space-time detectors. It appears that

Fig. 4 .

 4 Fig. 4. A ST vs C ST (dB), both without ZDC rejection -range values.

Fig. 5 .

 5 Fig. 5. Space-time CAF A ST (dB) with ZDC rejection.

Fig. 6 .

 6 Fig. 6. Space-time CHAD C ST (dB) with ZDC rejection.

Fig. 7 .

 7 Fig. 7. A ST vs C ST (dB), both with ZDC rejection -range values.

Fig. 8 .

 8 Fig. 8. Blind space-time adaptive CHAD C B-STAP (dB).

Fig. 9 .

 9 Fig. 9. A ST (dB) with ZDC rejection vs C B-STAP (dB) -range values.

Fig. 10 .

 10 Fig. 10. Space-time CAF A ST (dB) without ZDC rejection.

Fig. 11 .

 11 Fig. 11. Space-time CHAD C ST (dB) without ZDC rejection. reduction of the clutter floor level, hence two of the three aircraft returns are now visible. The third one (white circle) outreaches the clutter floor but remains hidden between important secondary lobes. As expected with channel-based approaches, the ghost returns no more appear. The surveillance map in the next figure 12 corresponds to a space-time CAF after ZDC rejection. Again, due to various impairments, disturbing residual leakages from the zero-Doppler contribution can be observed all along the Doppler dimension. Several new target returns are however found and those of the fighter aircraft appear now inside the black circles. One may still note the presence of duplicated ghost peaks caused by the incomplete rejection of ZDC.

Fig. 12 .

 12 Fig. 12. Space-time CAF A ST (dB) with ZDC rejection.

Fig. 15

 15 Fig. 15 shows the results obtained with both A ST (with ZDC rejection) and C B-STAP depending on the bistatic distance, along the -123 Hz Doppler frequency shift. It corresponds to the fighter aircraft expected return with the highest bistatic distance (76.8km). One can see that, around the

Fig. 14 .

 14 Fig. 14. Blind space-time adaptive CHAD C B-STAP (dB).

Fig. 15 .

 15 Fig. 15. A ST (dB) with ZDC rejection vs C B-STAP (dB) -range values.

Fig. 16 .

 16 Fig. 16. ZDC rejection + A ST (dB) -12 seconds aircraft tracking (2D view).

Fig. 17 .

 17 Fig. 17. C STAP (dB) -12 seconds aircraft tracking (2D view).

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.Citation information: DOI 10.1109/TSP.2017.2742980, IEEE Transactions on Signal Processing


Elnur Garayev

Gaëtan Herbette

Carole Di Giorgio

Philippe Chiffolleau

David Roux

Huguette Sallanon

Evelyne Ollivier

Riad Elias

Béatrice Baghdikian
email: beatrice.baghdikian@univ-amu.fr

New sesquiterpene acid and inositol derivatives

Keywords: Inula montana L., Sesquiterpenes, Inositol derivatives, Anti-inflammatory activity

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The Inula genus comprises more than one hundred species, mainly distributed in Africa, Asia and Europe, predominantly, in the Mediterranean Area. Compounds isolated from the species of this genus are mono-, sesqui-, di-and triterpenes, flavonoids and inositol derivatives. Cyclic sesquiterpenes are the predominant constituents of Inula genus [START_REF] Zhao | Chemical constituents of plants from the genus Inula[END_REF][START_REF] Seca | The genus Inula and their metabolites: From ethnopharmacological to medicinal uses[END_REF][START_REF] Wu | Antiinflammatory Inositol Derivatives from the Whole Plant of Inula cappa[END_REF]. Alcoholic and oil preparations of the flowering aerial parts of this plant are widely used in the traditional medicine of Provence (France) in the treatment of hematomas, as well as Arnica montana L. [START_REF] Amir | Les cueillettes de confiance[END_REF]. However, its phytochemical composition has been poorly investigated [START_REF] Romero | Phytochemistry and pharmacological studies of Inula montana[END_REF][START_REF] Reynaud | Note Free Flavonoid Aglycones from Inula montana[END_REF][START_REF] Belyagoubi-Benhammou | Phenolic contents and antioxidant activities in vitro of some selected Algerian plants[END_REF][START_REF] Martín | Screening for Protozoocidal Activity of Spanish Plants[END_REF]. Only six sesquiterpene lactones, four free flavonoid aglycones and volatile composition have been reported in Inula montana L. until now [START_REF] Romero | Phytochemistry and pharmacological studies of Inula montana[END_REF][START_REF] Reynaud | Note Free Flavonoid Aglycones from Inula montana[END_REF].

Antioxidant and anti-protozoocidal activities of Inula montana extracts have been previously reported as well. No anti-inflammatory activity has been investigated comparing to Inula cappa or Inula wissmanniana [START_REF] Wu | Antiinflammatory Inositol Derivatives from the Whole Plant of Inula cappa[END_REF][START_REF] Belyagoubi-Benhammou | Phenolic contents and antioxidant activities in vitro of some selected Algerian plants[END_REF][START_REF] Martín | Screening for Protozoocidal Activity of Spanish Plants[END_REF][START_REF] Wang | Chemical constituents from Inula wissmanniana and their anti-inflammatory activities[END_REF]. In this study, we describe the isolation of sixteen compounds for the first time from Inula montana L., including five new ones. The anti-inflammatory activity of the isolated compounds has been also evaluated.

Experimental part 2.1 General

Optical rotations were measured using Anton Paar polarimeter (l=10 mm, ∅=5 mm, Ref. 279986.05). UV spectra were recorded on Shimadzu UV-1650pc spectrophotometer. IR spectra were recorded on Bruker Vertex 70 FT-IR with ATR module. 1D and 2D NMR spectra were recorded on Bruker AVANCE III 600 spectrometer ( 1 H 600.13MHz) with tetramethylsilane (TMS) as internal standard at 300K. Chemical shifts (δ) were expressed in ppm with TMS as an internal reference. HR-ESI-MS was performed on SYNAPT G2 HDMS (Waters) spectrometer. UHPLC was performed on Agilent 1290 liquid chromatograph equipped with a Zorbax Eclipse Plus, C18 (100 × 2.1 mm, 2.8 µm) (Agilent) column. HPLC was performed on Agilent 1200 liquid chromatograph equipped with a Luna C18 (150 × 4.6 mm, 3 µm) column. Preparative HPLC was performed on Gilson PLC2020 with a Luna C18 (150 × 21.2 mm, 3 µm) column (Phenomenex). Flash chromatography was performed using a Teledyn Isco CombiFlash Rf 200 (USA). TLC analysis was performed on Silica F 254 plates (Merck).

The positive control dexamethasone, phosphate tampon, dimethyl sulfoxide, RAW 264.7 cells and Lyposaccharides (LPS) of E.coli were purchased from Sigma-Aldrich. Griess reagent was purchased from Promega.

Plant material

The plant I. montana was collected in Merindol (Luberon Natural Regional

Extraction and isolation

The air-dried plant materials: leaves (0.2 kg) and flowers (0.2 kg), were extracted separately with EtOH 95% (v/v) (1.2 L × 3), by percolation at room temperature for 18 h, to afford crude extracts E2 (leaves, 8.6 g) and L2 (flowers, 23.7 g). After concentration in vacuo, each extract was suspended in H 2 O and partitioned by petroleum ether and dichloromethane successively, to provide a petroleum ether E2P (2.1 g) and dichloromethane E2D (1.0 g) extracts for leaves and petroleum ether L2P (4.4 g) and dichloromethane L2D (0.7 g) extracts for flowers.

The leaves dichloromethane extract E2D (0.9 g) was subjected to reversed phase silica gel (C18, 100 g) flash chromatography and eluted with water/methanol MeOH-H 2 O (0-30 min 35-85% MeOH, 30-31 min 85-100% MeOH, 31-35 min 100% MeOH (v/v), flow rate: 40 mL/min) to afford three fractions (E2D1), (E2D2), (E2D3) and three compounds 6 (0.3 mg), 7 (19.2 mg) and 8 (0.3 mg). Fraction E2D1 (50.5 mg) was subjected to preparative HPLC (C18, 3 µm,150 × 21.2 mm, flow rate 10.6 mL/min, wavelength 225 nm) using MeOH-H 2 O system (52-48, v/v) which gave two compounds: 2 (12 mg) and 3 (12.7 mg). Fraction E2D2 (50.7 mg) was separated by preparative HPLC (C18, 3 µm,150 × 21.2 mm, flow rate 10.6 mL/min, wavelength 210 nm) using MeOH-H 2 O system (42-58, v/v) which afforded compounds 9 (0.3 mg), 10 (0.2 mg) and 11 (0.5 mg). Fraction E2D3 (12.7 mg) has been found to be as the mix of two compounds 4 and 5.

The flowers petroleum ether extract L2P (2.1 g) was subjected to silica gel (24g) flash chromatography and eluted with n-hexane/ethyl acetate n-Hexane-Ethyl acetate (EtOAc) (0-30 min 0-20% EtOAc, 30-42 min 20-100% EtOAc, 42-50 min 100% EtOAc (v/v), flow rate 20 mL/min) to give three fractions (L2P1), (L2P2) and (L2P3). Fraction L2P1 (350 mg) was subjected to silica gel (24g) flash chromatography and eluted with toluene/EtOAc (0 -20 min 0% EtOAc, 20 -45 min 0-5% EtOAc, 45 -50 min 5% EtOAc, 50 -55 min 5 -100 % EtOAc, 55 -62 min 100% EtOAc (v/v), flow rate 20 mL/min) to afford compound 12 (23,2 mg). Fraction L2P2 (48 mg) was separated by HPLC using as eluent water/methanol (C18, 3 µm, 150 × 21.2 mm, 0 -60 min, flow rate 21.2 mL/min, wavelength 210 nm) using MeOH-H 2 O system (52-48, v/v) which gave compound 13 (9.4 mg). Fraction L2P3 (50 mg) was separated by HPLC (C18, 3 µm, 150 × 21.2 mm, flow rate 21.2 mL/min, wavelength 210 nm) using MeOH-H 2 O system (0 -20 min 40 -100 % MeOH, 20 -30 min 100 % MeOH) which gave compound 1 (16.2 mg).

Fraction L2D (0.6 g) was subjected to reversed phase silica gel (C18, 100 g) flash chromatography and eluted with the same conditions as the fraction E2D to afford one fraction (L2D1) and two compounds 14 ( 2 and3 The concentrations of the test material causing respectively a 50% decrease of NO release (IC 50-NO release ) and a 50% decrease of cell viability (IC 50-cell viability ) were calculated through non-linear regression analysis using software Tablecurve Version 2.0. The anti-inflammatory ratio was defined as the ratio between anti-inflammatory activity and toxicity. It was expressed as follows:

Anti-inflammatory ratio = IC 50 cell viability / IC 50 NO release

Results and Discussion

One 5), together with eleven compounds 1β-Hydroxyarbusculin A (6) [START_REF] Clark | Microbial transformations of the sesquiterpene lactone costunolide[END_REF], Artemorin (7) [START_REF] Geissman | Sesquiterpene lactones of Artemisia-A. verlotorum and A. vulgaris[END_REF], Santamarin (8) [START_REF] Da Silva | 13C NMR spectra of natural products. 2-eudesmanolides[END_REF], Chrysosplenol C (9) [START_REF] Huong | Homogentisic Acid Derivatives from Miliusa balansae[END_REF], 6-Hydroxykaempferol 3,7-dimethyl ether (10) [START_REF] Bailian | Chemical constituents of Laggera pterodonta[END_REF],

Reynosin [START_REF] Geissman | Sesquiterpene lactones of Artemisia-A. verlotorum and A. vulgaris[END_REF] [15], Calenduladiol-3-palmitate (12) [START_REF] Öksüz | Triterpene fatty acid esters and flavonoids from Inula britannica[END_REF], Costunolide [START_REF] Huong | Homogentisic Acid Derivatives from Miliusa balansae[END_REF] [17], 4-Hydroxy-3,5-dimethoxybenzenemethanol ( 14) [START_REF] Chang | Chemical constituents of the stems of Celastrus rugosus[END_REF] and 9β-Hydroxycostunolide (15) [START_REF] Bohlmann | New sesquiterpene lactones from Inula species[END_REF] have been isolated for the first time from leaves and flowers of I. montana.

Among the known compounds, compounds 6 and 7 have been obtained from Inula genus for the first time [START_REF] Zhao | Chemical constituents of plants from the genus Inula[END_REF][START_REF] Seca | The genus Inula and their metabolites: From ethnopharmacological to medicinal uses[END_REF]. Known compounds have been identified by comparison of their spectroscopic data with those in the literature. Inositol derivatives with hemiterpenic acid moieties has been already described in Inula cappa, but they are not frequently found in genus Inula [START_REF] Wu | Antiinflammatory Inositol Derivatives from the Whole Plant of Inula cappa[END_REF][START_REF] Zou | Inositol angelates from the whole herb of Inula cappa[END_REF].

Previously, six sesquiterpene lactones [START_REF] Amir | Les cueillettes de confiance[END_REF] and identified in our study [START_REF] Reynaud | Note Free Flavonoid Aglycones from Inula montana[END_REF]. ) defined an isobutenic acid connected to the C 7 on the ten carbons ring.

An acetate moiety was observed and connected to the C 2 on the ten carbons ring by the presence of the 3 J HMBC correlation (H 2 -COO). The proton H 6ax at 1.94 (td J 12.6, 7.5) show a 3 J trans coupling constant of 12.6Hz with H 7 in axial position at 2.39 (m) and the proton H 2 at 5.68 (br td J 10.6, 5.3) show two 3 J trans coupling constant of 10.6 Hz with H 1 at 5.03 (br d J 10.6) and H 3ax at 2.18 (br t J 11.1), so H 2 is in axial position. NOESY spectra showed NOE correlation between H 2ax and H 14 and H 15 methyls groups. Between the four possible conformations of germacrene moiety described by de Kraker et al., the only possibility is the conformation noted "UU" with the orientation of C-14 and C15 methyls groups to Upside [START_REF] De Kraker | Germacrenes from fresh costus roots[END_REF]. The other NOE correlations observed between H 1 -H 3ax , H 3ax -H 5 , H 5 -H 7ax , H 1 -H 5 give the stereochemistry of 1 (Fig. 3). Compound 1 was previously proposed by Zdero et al., as a possible intermediate compound in the transformation of germacrene A acid to the desacetyl derivative of compound 1, but it has never been isolated [START_REF] Bohlmann | New sesquiterpene lactones from Inula species[END_REF]. In our study, the structure of compound 1 isolated from I. montana is elucidated as shown, and this compound is called Eldarin.

Compound 2 (Fig. For compound 5, the 1 H NMR spectrum showed the second difference with the spectrum of compound 3 with two methyl resonances at 0.89 ppm (each d, J 6.7), one methylene with two resonances at 2.12 ppm (dd, J 14.9, 7.4), 2.09 ppm (dd, J 14.9, 6.8) and one methine resonance at 1.96 ppm (m) linked to carbonyl ester at 172.9 ppm corresponding of an isovaleroyl moiety. The HMBC spectrum (Fig. 3) showed that H-6 at 5.65 ppm (t, 10.0) is connected with the isovaleroyl carbonyl at 172.9 ppm, thus, compound 5 was established as myoinositol-1-angelate-4,5-diacetate-6isovalerate. Anti-inflammatory activity and cytotoxicity of nine isolated compounds have been tested in vitro comparing to dexamethasone as a positive control. The results are summarized in Table 4. The new inositol derivatives 2 and 3, sesquiterpene lactones

  Park, France) in June 2015 and identified by Dr. Didier Morisot from the University of Montpellier and by Mr. Laurent Michel from Luberon Natural Reserve. Voucher specimens of leaves (IMFF151) and flowers (IMFLF151) were deposited in the Laboratory of Pharmacognosy of Aix Marseille University (Marseille, France).

  ; HR-ESI-MS: m/z 431.1908 [M + H] + (calcd. for C 20 H 31

2. 4

 4 Anti-inflammatory and cytotoxicity assaysIn vitro anti-inflammatory assay is based on the ability of immortalized mouse macrophages RAW 264.7 to generate a strong inflammatory response leading to NO release, after stimulation by antigens of E. coli. Mouse macrophages were seeded into 48-well tissue culture plates at the concentration of 1.10 5 cells/ml (200µl/well) for 24 hours at 37°C (5% CΟ 2 ). At the end of the incubation period, the culture medium was replaced by 200µl of medium containing appropriate concentrations of evaluated compound, and cells were incubated at 37°C (5% CO 2 ) for one hour. Experiments were performed in duplicate and dexamethasone was used as a positive control at the concentrations of 1, 5, 10, 50 and 100 µM. At the end of the incubation period, proinflammatory LPS from E.coli were added to the cell cultures (1 µg/ml). Then the cells were incubated at 37°C (5% CO 2 ) for 18 hours. NO release was measured in the culture supernatant by the Griess reaction: 50 µl of the supernatants were transferred into the 96-well tissue culture plate, and 50 µl of the Griess modified reagent were added into each well. After a 15 min period at room temperature, Optical Density (OD) of each well was read at 540 nm by fluorescence -luminescence reader Infinite M200 Pro (TECAN). Results obtained for wells, treated with evaluated compound, were compared to untreated control wells (DMSO, 100% viability) and converted into the percentage values.Cell viability was measured to validate this assay: the WST-1 vital dye reagent was used to measure mitochondrial respiration. For this purpose, the culture medium was decanted and 50 µl of the WST-1 reagent (1/10 dilution in phosphate buffer) were added into each well. After a 30 min incubation period at 37°C (5% CO 2 ), OD of each well was read at 450 nm by a fluorescence -luminescence reader Infinite M200 Pro (TECAN). The results obtained for wells, treated with evaluated compound, were compared to untreated control wells (DMSO, 100% viability) and converted into the percentage values. Inhibition of NO release and inhibition of cell viability were expressed as percentages comparing to the negative controls: Percentage of NO release or cell viability = × ( )

Fig. 1 .Fig. 2 .

 12 Fig. 1. Structures of the isolated compounds 1, 6-16 from I. montana.

Fig. 3 .

 3 Fig. 3. Keys COSY, HMBC and NOE correlations of compounds 1 -5.

7, 13

 13 and 15 and phenolic compounds 14 and 16 showed release of NO with IC 50 value < 30 µM on macrophages RAW 264.7, while compounds 1 and 12 have not shown neither anti-inflammatory activity, nor cytotoxicity at the concentrations at 51.34 and 22.04 µM respectively. Compound 15 exhibited cytotoxicity with IC 50 value 1.21 µM.Comparing the anti-inflammatory ratio we can conclude that compounds 2, 7, 13, 14 and 16 present promised anti-inflammatory activity with the anti-inflammatory ratio higher than the positive control dexamethasone.

Table 1 ;

 1 HR-ESI-MS: m/z 293.1748 [M + H] + (calcd. for C 17 H 25 O 4 .3.2 Myoinositol,1,5-diangelate-4,6-diacetate (2) Yellow oil; [α] -8,35 (с 1.1, MeOH); UV λ max (log ε) 217 nm (3.6; MeOH); IR (ATR) ν max 3472, 3022, 2928, 1751, 1719, 1647, 1457, 1365, 1223, 1138, 1083, 1037 cm -1 ; 1 H and 13 C NMR data, see Tables 2 and 3; HR-ESI-MS: m/z 429.1756 [M + H] + (calcd. for C 20 H 29 O 10 MeOH); UV λ max (log ε) 217 nm (4.0; MeOH); IR (ATR) ν max ~3373, 2926, 1706, 1645, 1457, 1381, 1352, 1230, 1146, 1084, 1042 cm -1 ; 1 H and 13 C NMR data, see Tables 2 and 3; HR-ESI-MS: m/z 429.1757 [M + H] + (calcd. for C 20 H 29 O 10

	15 mg) and 15 (5 mg). The

+ : 293.1747). 2+ : 429.1755). 2.3.4 Myoinositol-1-angelate-4,5-diacetate-6-(2-methylbutirate) (4) and myoinositol-1-angelate-4,5-diacetate-6-isovalerate (5) Yellow oil; IR (ATR) ν max 3473, 2964, 2933, 1746, 1647, 1460, 1366, 1226, 1152, 1036 cm -1 ; 1 H and 13 C NMR data, see Tables

Table 1

 1 NMR data of compound 1. 1 H (600MHz),13 C (150MHz) in CDCl 3 at 300K .

	No.	δ 1H	δ 13C
	1ax	5.03 (br d 10.6)	128.1
	2ax	5.68 (br td 10.6, 5.3)	69.7
	3ax	2.18 (br t 11.1)	44.7
	3eq	2.58 (dd 11.1, 5.3)
	4	-	134.4
	5ax	5.34 (t 7.5)	129.5
	6ax	1.94 (td 12.6, 7.5)	32.4
	6eq	2.22 (m)
	7ax	2.39 (m)	39.4
	8ax	1.86 (br td 13.8, 3.6)	34.6
	8eq	1.67 (m)
	9ax	1.77 (br td 14.0, 4.3)	34.6
	9eq	2.32 (br d 13.8)
	10		140.3
	11		146.0
	12		171.8
	13	6.29 (br s)	125.4
		5.66 (br s)
	14	1.86 (s)	22.5
	15	1.59 (s)	16.8
	2-CH 3 CO 2 2.05 (s)	21.5
		-	171.4

δ in ppm, br: broad, s: singlet, d: doublet, t: triplet, q: quadruplet, m: multiplet, (J Hz)

Table 2

 2 NMR data of compounds 2, 3, 4 and 5. 1 H (600MHz) in CD 3 OD at 300K .

	No.	2	3	4	5
		5.02 (dd, 10.0, 2.6)	5.09 (dd, 10.0, 2.6)	5.05 (dd, 10.0, 2.6)	5.02 (dd, 10.0, 2.6)
		4.23 (t, 2.6)	4.20 (t, 2.6)	4.16 (t, 2.6)	4.18 (t, 2.6)
		3.84 (dd, 10.0, 2.6)	3.83 (dd, 10.0, 2.6)	3.82 (dd, 10.0, 2.6)	3.81 (dd, 10.0, 2.6)
		5.44 (t, 10.0)	5.40 (t, 10.0)	5.37 (t, 10.0)	5.37 (t, 10.0)
		5.26 (t, 10.0)	5.22 (t, 10.0)	5.19 (t, 10.0)	5.16 (t, 10.0)
		5.68 (t, 10.0)	5.73 (t, 10.0)	5.65 (t, 10.0)	5.65 (t, 10.0)
			Ang-1
		-	-	-	-
		-	-	-	-
		6.18 (qq, 7.5, 1.5)	6.15 (qq, 7.5, 1.5)	6.15 (qq, 7.5, 1.5)	6.18 (qq, 7.5, 1.5)
		1.96 (dq, 7.5, 1.5)	1.94 (ov)	1.94 (ov)	1.97 (dq, 7.5, 1.5)
		1.86 (br quint, 1.5)	1.83 (br quint, 1.5)	1.83 (br quint, 1.5)	1.85 (br quint, 1.5)
			Ac-4
		-	-	-	-
		2.01 (s)	2.04 (s)	2.04 (s)	2.03 (s)
		Ang-5	Ac-5
		-	-	-	-
		-	1.90 (s)	1.90 (s)	1.95 (s)
		6.13 (qq, 7.5, 1.5)
		1.92 (ov)
		1.80 (br quint, 1.5)
		Ac-6	Ang-6	2MB-6	iVal-6
		-	-	-	-
		1.93 (s)	-	2.29 (m)	2.12 (dd, 14.9, 7.4)
					2.09 (dd, 14.9, 6.8)
			6.09 (qq, 7.5, 1.5)	1.58 (m), 1.38 (m)	1.96 (m)
			1.88 ((dq, 7.5, 1.5)	0.83 (t, 7.4)	0.89 (d, 6.7)
			1.77 (br quint, 1.5)	1.03 (d, 7.0)	0.89 (d, 6.7)

δ in ppm, br: broad, s: singlet, d: doublet, t: triplet, q: quadruplet, m: multiplet, ov: overlapped (J Hz)

Table 3

 3

	NMR data of compounds 2, 3, 4 and 5. 13 C (150MHz) in CD 3 OD at 300K .
	No.	2	3	4	5
	1	73.0	72.6	72.6	72.6
	2	71.1	71.0	71.0	70.9
	3	70.8	70.5	70.5	70.4
	4	73.9	73.7	73.8	73.8
	5	72.4	72.8	72.7	72.8
	6	71.4	70.5	70.7	70.7
			Ang-1
	1	168.3	167.9	167.6	167.8
	2	128.6	128.3	128.1	128.1
	3	140.4	140.1	140.9	140.6
	4	15.9	15.7	16.0	16.0
	5	20.6	20.3	20.5	20.5
			Ac-4
	1	172.0	171.8	171.8	171.7
	2	20.9	20.6	20.6	20.6
		Ang-5		Ac-5
	1	168.0	171.2	171.1	171.1
	2	128.4	20.3	20.5	20.5
	3	140.2
	4	16.1
	5	20.5
		Ac-6	Ang-6	2MB-6	iVal-6
	1	171.3	167.7	176.5	172.9
	2	20.7	128.3	42.3	42.3
	3		139.6	27.3	26.4
	4		15.5	11.7	22.3
	5		20.2	16.9	22.3

δ in ppm, br: broad, s: singlet, d: doublet, t: triplet, q: quadruplet, m: multiplet, ov: overlapped (J Hz)

  -C 10 , H 15 -C 3 , H 15 -C 5 , H 14 -C 9 , H 14 -C 1 ) correlations (Fig.3) had to define a ten carbon ring. The second moiety with based on HMBC correlations (H 13 -C 11 , H 13 -C 12 , H 13 -C 7

	four methines including two olefinic carbons (129.5, 128.1 ppm), one oxygenated sp 3
	carbon (69.7 ppm), one sp 3 carbon (39.4 ppm), five methylenes including one
	methylene sp 2 carbon (125.4 ppm), four methylene sp 3 carbons (44.7, 34.6, 34.6,
	1 32.4ppm), three methyls (22.5, 21.5, 16.8 ppm). The 1 H NMR spectrum indicated the 6 7 8
	presence of one methylene 6.29 ppm (br s), and 5.66 ppm (br s), one methine bearing
	by an oxygen 5.68 ppm (br td, J 10.6, 5.3), two ethylenic protons 5.34 (t 7.5) and
	5.03 ppm (br d J 10.6), four methylenes 2.58 (dd J 11.1, 5.3) and 2.18 (br t J 11.1),
	9 2.22 (m) and 1.94 (td J 12.6, 7.5), 1.86 (br td J 13.8, 3.6) and 1.67 (m), 2.32 (br d J 10 11 12
	HO 13.8) and 1.77 (br td J 14.0, 4.3), one methine 2.39 (m), three methyls 1.86 (s), 1.59 O OH
	O (s) which one from acetate group 2.05 (s). The spectroscopic data suggested a O OH
	13 germacrene skeleton: the COSY (H 1 -H 2 , H 2 -H 3 , H 5 -H 6 , H 6 -H 7 , H 7 -H 8 , H 8 -H 9 ) and the 14 15 16
	HMBC (H 2

  m/z 429.1756 [M+H] + (calcd. for C 20 H 29 O 10 + 429.1755), with the existence of seven degrees of unsaturation. The IR spectrum suggested the presence of hydroxyl groups (3472 cm -1 ), carbonyl groups of ester function (1751, 1719 and 1222 cm -1 ), double-bond groups (3022, 1647cm -1 ) and methyl groups (1365 cm -1 ). (Fig.1) has been obtained as a mix with compound 5 as yellow oil. The molecular formula C 20 H 30 O 10 was established by positive HR-ESI-MS , which gave a pseudo-molecular ion peak at m/z 431.1908 [M+H] + (calcd. for

	connected with an angeloyl carbonyl at 167.7 ppm. Thus, compound 3 was
	established as myoinositol-1,6-diangelate-4,5-diacetate.
	The 1 H and COSY NMR spectra indicated the presence of myoinositol structure with H-1 axial at 5.02 ppm (dd, J 10.2, 2.6 Hz), H-2 equatorial at 4.23 ppm (t, J 2.6 Hz), Compound 4 C 20 H 31 O 10 + 431.1912), with an existence of six degrees of unsaturation. The IR, 1 H H-3 axial at 3.84 ppm (dd J 10.0, 2.6 Hz), H-4 axial at 5.44 ppm (t, J 10.0 Hz), H-5 and 13 C NMR spectra are close to compound 3 with an exception for compound 4 axial at 5.26 (t, J 10.0 Hz), and H-6 axial at 5.68 ppm (t J 10.0 Hz). In the HMBC with the presence of a hydrogenated double bond of one angeloyl moiety. The 1 H spectrum, the two 1 H resonances at 1.93 and 2.01 ppm (each 3H, s) are correlated to NMR spectrum showed two methyl resonances at 0.83 ppm (t, J 7.4), 1.03 ppm (d, J two carbonyl carbons at 171.3 and at 172.0 ppm respectively indicate two acetate 7.0), one methylene with two resonances at 1.58 ppm (m), 1.38 ppm (m) and one moieties. Two other 1 H resonances at 1.92 and at 1.96 ppm (each 3H, dq J 7.5, 1.5) methine resonance at 2.29 ppm (m) linked to carbonyl ester at 176.5 ppm correlated to each of two olifenic carbon at 128.4, 140.2 ppm and at 128.6, 140.4 corresponding to the 2-methylbutiroyl moiety. The HMBC spectrum (Fig. 3) showed ppm respectively, and the two last 1 H resonances at 1.80 and 1.86 (each 3H, br quint that H-6 at 5.65 ppm (t, J 10.0) is connected with the 2-methylbutiroyl carbonyl at J 1.5) correlated to each of two same olifenic carbons respectively, which indicated 176.5 ppm, thus, compound 4 was established as myoinositol-1-angelate-4,5-the presence of two angelate moieties. The four chemical shifts downfield at 5.02 ppm (H-1), 5.26 ppm (H-5), 5.44 ppm (H-4) and at 5.68 ppm (H-6) were connected diacetate-6-(2-methylbutirate).
	to two acetate moieties with H-4 and H-6 respectively by the presence of HMBC
	(Fig. 3) correlation with carbonyl carbons at 172.0 and 171.3 ppm respectively, and
	were connected to two angeloyl moieties with H-1 and H-5 by the presence of
	HMBC correlation with carbonyl carbons at 168.3 and 168.0 ppm respectively. The
	NOE correlations (Fig. 3) between H-1, H-5 with the methyl groups of angeloyl and
	H-4, H-6 with the methyl group of acetyl confirmed the structure of compound 2 as
	myoinositol-1,5-diangelate-4,6-diacetate.
	Compound 3 (Fig. 2) was obtained as yellow oil. Its molecular formula
	C 20 H 28 O 10 was established by positive HR-ESI-MS, which gave a pseudo-molecular
	ion peak at m/z 429.1750 [M+H] + (calcd. for C 20 H 29 O 10	+ 429.1755), with the
	existence of seven degrees of unsaturation. The IR, 1 H and 13 C NMR spectra are close
	to compound 2 with the presence of two angeloyl, two acetate moieties and one
	myoinositol structure. The main difference observed is a substituent pattern of
	inositol ring: the HMBC spectrum (Fig. 3) showed that H-5 at 5.22 ppm (t, J 10.0) is
	connected with an acetate carbonyl at 171.2 ppm, and H-6 at 5.73 ppm (t, J 10.0) is

2) was obtained as yellow oil. Its molecular formula C 20 H 28 O 10 was established by positive HR-ESI-MS, which gave a pseudo-molecular ion peak at

Table 4

 4 Anti-inflammatory, cytotoxic activity and anti-inflammatory ratio of compounds 1

-3, 7, 12-16.

  The IC 50 values of compounds 1 and 12 have not been detected at the concentration of 51.34 and 22.04 µM respectively.

	S22. HR-ESI-MS Spectrum of Compound 3
	Compounds	NO release IC 50 (µM) a	Toxicity IC 50 (µM)	Anti-inflammatory ratio
	1	>51.34		>51.34	-
	2	3.53 +0.97	213.93 + 24.5	60
	3	27.37 + 6.7	144.1 + 13.7	5
	7	0.16 + 0.07	18.93 + 3.1	126
	12	>22.04		>22.04	-
	13	0.3 + 0.07	17.78 + 1.4	59
	14	1.63 + 0.55	178.08 + 24.1	109
	15	0.68 + 0.09	1.21 + 0.14	2
	16	0.4 + 0.08	22.98 + 5.7	57
	Dexamethasone b	4.33		163.33	38
			m/z	error (mDa)	error (ppm)
	Teoretical values b Positive control	1	429.1755 429.1749	--0.6	--1.4
	Experimental values	2	429.1750	-0.5	-1.2
		3	429.1750	-0.5	-1.2
	For

a

mix of Compound 4 and Compound 5: S23. 1 H NMR Spectrum (600 MHz, CD 3 OD) of mix of Compound 4 and Compound 5 S24. DEPTQ135 Spectrum of mix of Compound 4 and Compound 5

	FEDIT_D3AA_CONE10_MEX2_copy 10 (0.188) AM2 (Ar,18000.0,0.00,0.00); Cm (1:10)							TOF MS ES+
		387.1649										6.32e5
	100
													442.3374
					404.1914		411.1643
	%									429.1750
	384.2956
	0												m/z
	380	385	390	395	400	405	410	415	420	425	430	435	440

S16. 1 H NMR Spectrum (600 MHz, CD 3 OD) of Compound 3 S17. 13 C NMR Spectrum (150 MHz, CD 3 OD) of Compound 3 S18. HMBC Spectrum of Compound 3

S19. 1 H-1 H COSY Spectrum of Compound 3

Acknowledgements

The work was supported by the region PACA (France) (grant number 2013-08504), the Embassy of France in Azerbaijan and the Luberon Natural Regional Park in France.

We are grateful to Mr. Vincent Lorenzini from the Embassy of France in Azerbaijan for his contribution on funding of this research project work, to Dr.

Valérie Monnier, to Dr. Jean-Valère Naubron and Dr. Christophe Chendo from Spectropole laboratory (France) for the spectroscopic analysis and to Dr. Didier Morisot and Mr. Laurent Michel for their botanical identification.

Conflict of Interest Statement

The authors have declared that there is no conflict of interest.

S1. 1 H NMR Spectrum (600 MHz, CDCl 3 ) of Compound 1 S2. 13 C NMR Spectrum (150 MHz, CDCl 3 ) of Compound 1 S3. HSQC Spectrum of Compound 1 S4. 1