Target detection in non-stationary clutter background and Riemannian geometry

28/08/2013
Auteurs : Haiyan Fan
OAI : oai:www.see.asso.fr:2552:5122
DOI :

Résumé

Target detection  in non-stationary clutter background and Riemannian geometry

Métriques

1067
166
3.17 Mo
 application/vnd.openxmlformats-officedocument.presentationml.presentation
bitcache://e9c3a94f84d6490d2974980134752ffd249d5663

Licence

Creative Commons Aucune (Tous droits réservés)

Sponsors

Sponsors scientifique

logo_smf_cmjn.gif

Sponsors financier

logo_gdr-mia.png
logo_inria.png
image010.png
logothales.jpg

Sponsors logistique

logo-minesparistech.jpg
logo-universite-paris-sud.jpg
logo_supelec.png
Séminaire Léon Brillouin Logo
logo_cnrs_2.jpg
logo_ircam.png
logo_imb.png
<resource  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
                xmlns="http://datacite.org/schema/kernel-4"
                xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4/metadata.xsd">
        <identifier identifierType="DOI">10.23723/2552/5122</identifier><creators><creator><creatorName>Haiyan Fan</creatorName></creator></creators><titles>
            <title>Target detection  in non-stationary clutter background and Riemannian geometry</title></titles>
        <publisher>SEE</publisher>
        <publicationYear>2013</publicationYear>
        <resourceType resourceTypeGeneral="Text">Text</resourceType><dates>
	    <date dateType="Created">Tue 15 Oct 2013</date>
	    <date dateType="Updated">Mon 25 Jul 2016</date>
            <date dateType="Submitted">Fri 25 May 2018</date>
	</dates>
        <alternateIdentifiers>
	    <alternateIdentifier alternateIdentifierType="bitstream">e9c3a94f84d6490d2974980134752ffd249d5663</alternateIdentifier>
	</alternateIdentifiers>
        <formats>
	    <format>application/vnd.openxmlformats-officedocument.presentationml.presentation</format>
	</formats>
	<version>9942</version>
        <descriptions>
            <description descriptionType="Abstract"></description>
        </descriptions>
    </resource>
.

Target detection in non-stationary clutter background and Riemannian geometry Haiyan Fan 2013.05.21 Contents 1 Background 2 Methodology & Technology Road 3 Experiment Program & Results ContentsContents 4 Conclusions Background BackgroundBackground  The emerging of Riemannian geometry approach brings out a new era of statistical signal processing  The emerging of Riemannian geometry approach brings out a new era of statistical signal processing  Non-stationary signal detection is gradually gaining importance  Non-stationary signal detection is gradually gaining importance Many kinds of signal we meet today are non-stationary, for example  Non-Gaussian sea clutter has essential non-stationarity  Ultrasound Doppler Signals, obtained from the Physiological flows, are also non-stationary  Riemannian manifold has a more natural description of the signal structure  Barbaresco et al. has done much work in applying Riemannian metric to target detection of Radar signal Background Background  Existing methods Review The understanding of the sentence “Riemannian manifold has a more natural description of the signal structure”  Measured signals often belong to manifolds that are not vector spaces. In that case, processing the signal in flat Euclidean space is imprecise.  Riemannian manifold satisfies the invariance requirements to build some statistical tools on transformation groups and homogeneous manifolds that avoids paradoxes. Background  Existing methods Review The RG approach proposed by Barbaresco Autoregressive coefficient parameterization Riemannian metric & Riemannian distance Riemannian geodesico Riemannian median Targets detection Step 1 Step 2 Step 3 Step 4Step 5 In Step 1, a regularized Burg algorithm was used for parameterization of the signal by Barbaresco. Then the signal is mapped into a complex Riemannian manifold identified by the autoregressive coefficients. Riemannian distance and Riemannian median is derived for the manifold. The Principle of targets detection is : if a location has a good Riemannian distance from its Riemannian median, targets are supposed to appear in this location. Background  Existing methods Review Methodology & Technology Road RG+SLAR Smooth Prior long AR model (SLAR) Riemannian geometry method (RG) Based on Barbaresco’s work, we extend the Riemannian geometry method for targets detection of non-stationary signal Methodology & Technology Road Better accommodate the non-stationarity of signal Inherit the RG technical road of Barbaresco Methodology  smooth prior long AR model (SLAR) Pseudo-stationary spectral analysis for non-stationary signal in short analysis window Large model order fitted to relatively short analysis window Smoothness constraint to overcome the ill-posedness and spurious peaks brought by high order SLAR model Avoid underestim ation of m odel order as order selection criterion does Methodology  Riemannian geometry approach(RG) Observation flow Complex Riemannian manifold Autoregressive coefficient parameterization by SLAR model SLAR part ... Guard cell Cell under test Guard cell …1R iR 1iR NR Computing Riemannian median Riemannian distance Threshold Detec tion RG approach Threshold is set by empirical value Technology roadmap Experiment Program & Results Experiment Program Experiment Program One typical instance of target detection in non-stationary clutter background is the problem of radar detection in non-Gaussian sea clutter the experiment part will use target detection in the presence of non-Gaussian sea clutter to demonstrate the performance of our proposed method  Numeric experiments: simulated examples are given to validate performance of RG+SLAR method proposed in the paper, by comparing with Doppler filtering with DFT method and the RG approach with Regularized Burg algorithm(RG+ReBurg)  Real targets detection: RG+SLAR method will applied to real target detection within sea clutter with McMaster IPIX radar data. Experiment Results  Numeric experiment results The simulated Radar & targets parameters: Table 1 Radar Parameters Carrier Frequency Bandwidth Pulse repetition frequency Unambiguous Range interval Unambiguous velocity interval 10Ghz 10Mhz 10Khz 15Km 150m/s Table 2 Target Parameters Range SNR Rel_RCS velocity 2km -3dB -26.7dB 60m/s 3.8km 5dB -7.55dB 30m/s 4.4km 10dB 0dB -30m/s 4.4km 7dB -3dB -60m/s [1] SNR is the abbreviation of “Signal to Noise Ratio”. [2] Rel_RCS means relative RCS,. The relative RCS is RCS/ max (RCS) in dB. Max (RCS) is the maximum RCS of the 4 targets. Experiment Results  Numeric experiment results RG+SLAR Figure 1 the range-velocity map of clutter cancelled data obtained from SLAR modeled spectral estimation. Here, the velocity axis is linearly mapped from the frequency. , is the speed of light, is the carrier frequency. Figure 2 range-velocity map obtained from the Riemannian median of the clutter canceled data based on the reflection coefficients parameterization using SLAR model Experiment Results  Numeric experiment results RG+SLAR Table 3 Detected Target Parameters (RG+SLAR) Range Rel_RCS velocity 2km -30.8dB 61.81m/s 3.8km -12.5dB 31.39m/s 4.4km 0dB -29.89m/s 4.4km -3.38dB -62.05m/s Figure 3 Range with targets using RG+SLAR method Experiment Results  Numeric experiment results Doppler filtering method & RG+ReBurg Figure 5 the Range-velocity map of clutter cancelled data. (a) The Range-velocity map of clutter cancelled data through spectral estimation of each range bin using regularized Burg algorithm. (b) The Range-velocity contour using Doppler filtering in the slow time. Experiment Results  Numeric experiment results Doppler filtering method & RG+ReBurg Figure 6 the ambient estimation of clutter cancelled data. (a) The Range-velocity map of the Riemannian median of clutter-cancelled data parameterized by RG+ReBurg method (b) The estimation using Doppler filtering method. Experiment Results  Numeric experiment results Doppler filtering method & RG+ReBurg Figure 7 detected Range peaks (a) The Range peaks detected by RG+ReBurg method. (b) The Range peaks detected by Doppler filtering method. Experiment Results  Real targets detection The measured data we use is the file 19931118_023604_stare C0000.cdf collected by McMaster IPIX radar. Table 4 IPIX Radar Parameters Environment Value Geometry Value Radar Value Wind condition 0~60km/h Antenna azm. 170.2606º Unambig. Vel. 7.9872m/s Wind gust 90km/h Antenna elv. 359.5605º Range res. 15m Wave condition 0.8~3.8m Beam width 0.9º Carrier freq. 9,39Ghz Wave peak 5.5m Antenna gain 45.7dB PRF 1Khz [1] Unambig. Vel. is the abbreviation of “Unambiguous velocity”. [2] Range res. is the abbreviation of “Range resolution”. The average target to clutter ratio varies in the range 0-6 dB, and only one weak static target with small fluctuation is available in the range bin 8 (Primary target bin), with neighboring range bins 7-10, where the target may also be visible (Secondary target bin). Experiment Results  Real targets detection Real targets detection results Figure 8 (a) is the Range-velocity contour of pre-processed data (b) The ambient estimation of pre-processed data based on the reflection coefficients parameterization using SLAR Experiment Results  Real targets detection Real targets detection results Figure 9 Range bins with target. Primary target bin appears in range bin 8; the secondary target region spreads in 7-9 range bins. Figure 10 the velocity detection of the primary range bin 8 Conclusions Conclusions Conclusions A. Numeric and Real target detection experiments show that the proposed RG+SLAR method can attenuate the contamination brought by non-stationary clutter disturbance. B. The statistic depicting based on Riemannian geometry has higher accuracy of target detection than Doppler filtering based on DFT dose. C. The innovative idea of combing SLAR model and Riemannian geometry can achieve precise measurement of target location and velocity for non-stationary signal. Acknowledgement ! Reflection coefficients parameterization Riemannian metric Geodesic Riemannian median p 1 Step 2 Step 4 Riemannian distance ... CUT … Observation flow Complex Riemannian manifold Reflection coefficients parameterization by SLAR model 1θ iθ 1iθ Nθ Computing Riemannian median RG approach Riema dist ...... ...... guard cells guard cells threshold