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Robust PCA for Ground Moving Target Indication in Wide-Area Surveillance Radar System
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作者 Qingna Li He Yan +1 位作者 Leqin Wu Robert Wang 《Journal of the Operations Research Society of China》 EI 2013年第1期135-153,共19页
Robust PCA has found important applications in many areas,such as video surveillance,face recognition,latent semantic indexing and so on.In this paper,we study its application in ground moving target indication(GMTI)i... Robust PCA has found important applications in many areas,such as video surveillance,face recognition,latent semantic indexing and so on.In this paper,we study its application in ground moving target indication(GMTI)in wide-area surveillance radar system.MTI is the key task in wide-area surveillance radar system.Due to its great importance in future reconnaissance systems,it attracts great interest from scientists.In(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013),the authors first introduced robust PCA to model the GMTI problem,and demonstrate promising simulation results to verify the advantages over other models.However,the robust PCA model can not fully describe the problem.As pointed out in(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013),due to the special structure of the sparse matrix(which includes the moving target information),there will be difficulties for the exact extraction of moving targets.This motivates our work in this paper where we will detail the GMTI problem,explore the mathematical properties and discuss how to set up better models to solve the problem.We propose two models,the structured RPCA model and the row-modulus RPCA model,both of which will better fit the problem and take more use of the special structure of the sparse matrix.Simulation results confirm the improvement of the proposed models over the one in(Yan et al.in IEEE Geosci.Remote Sens.Lett.,10:617–621,2013). 展开更多
关键词 ground moving target indication Alternating direction method Wide-area surveillance radar system Joint sparsity Matrix recovery
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AN IMPROVED SAR-GMTI METHOD BASED ON EIGEN-DECOMPOSITION OF THE SAMPLE COVARIANCE MATRIX 被引量:1
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作者 Tian Bin Zhu Daiyin Zhu Zhaoda 《Journal of Electronics(China)》 2010年第3期382-390,共9页
An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function... An improved two-channel Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) method based on eigen-decomposition of the covariance matrix is investigated. Based on the joint Probability Density Function (PDF) of the Along-Track Interferometric (ATI) phase and the similarity between the two SAR complex images, a novel ellipse detector is presented and is applied to the indication of ground moving targets. We derive its statistics and analyze the performance of detection process in detail. Compared with the approach using the ATI phase, the ellipse detector has a better performance of detection in homogenous clutter. Numerical experiments on simulated data are presented to validate the improved performance of the ellipse detector with respect to the ATI phase approach. Finally, the detection capability of the proposed method is demonstrated by measured SAR data. 展开更多
关键词 ground moving target indication Sample covariance matrix Eigen-decomposition Ellipse detector
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