Robust Radial Velocity Estimation of Moving Targets Based on Adaptive Data Reconstruction and Subspace Projection Algorithm

In practice, inevitable image coregistration error and channel phase mismatch will significantly degrade the estimation performance of the target radial velocity in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. To overcome this problem, a new radial velocity estimation method using the subspace projection (SP) algorithm with adaptive data reconstruction is proposed in this letter. Based on the joint-pixel signal model, the Wiener weight vector is used to reconstruct the multichannel data vector of the pixel containing the moving target. Then, the SP algorithm is adopted to deal with the radial velocity estimation with the reconstructed single “snapshot” data. The validity and robustness are verified by both simulations and real SAR data experiments.

[1]  R. Klemm.  Principles of Space-Time Adaptive Processing , 2002 .

[2]  Gilda Schirinzi,et al.  GLRT Detection of Moving Targets via Multibaseline Along-Track Interferometric SAR Systems , 2012, IEEE Geoscience and Remote Sensing Letters.

[3]  C. H. Gierull.  Statistical analysis of the eigenvector projection method for adaptive spatial filtering of interference , 1997 .

[4]  Marina V. Dragosevic,et al.  Space-Based Motion Estimators—Evaluation With the First RADARSAT-2 MODEX Data , 2009, IEEE Geoscience and Remote Sensing Letters.

[5]  Ishuwa C. Sikaneta,et al.  Optimum SAR/GMTI Processing and Its Application to the Radar Satellite RADARSAT-2 for Traffic Monitoring , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Mehrdad Soumekh.  Signal subspace fusion of uncalibrated sensors with application in SAR and diagnostic medicine , 1999, IEEE Trans. Image Process..

[7]  Daniel R. Fuhrmann,et al.  A CFAR adaptive matched filter detector , 1992 .

[8]  Hai Li,et al.  Estimation Method for InSAR Interferometric Phase Based on Generalized Correlation Steering Vector , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Guisheng Liao,et al.  Optimum Data Vector Approach to Multibaseline SAR Interferometry Phase Unwrapping , 2009, IEEE Geoscience and Remote Sensing Letters.

[10]  João R. Moreira,et al.  A new MTI-SAR approach using the reflectivity displacement method , 1995, IEEE Trans. Geosci. Remote. Sens..

[11]  Ilan Ziskind,et al.  Maximum likelihood localization of multiple sources by alternating projection , 1988, IEEE Trans. Acoust. Speech Signal Process..

[12]  Guisheng Liao,et al.  Reduced-Dimensional Processing for Ground Moving Target Detection in Distributed Space-Based Radar , 2007, IEEE Geoscience and Remote Sensing Letters.

[13]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[14]  Yingning Peng,et al.  Ground Moving Target Signal Analysis in Complex Image Domain for Multichannel SAR , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[15]  C. Gierull,et al.  SAR-GMTI concept for RADARSAT-2 , 2004 .

[16]  Shen Chiu,et al.  Detection and Estimation With RADARSAT-2 Moving-Object Detection Experiment Modes , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Marina Dragosevic.  GLRT for two moving target models in multi-aperture SAR imagery , 2012 .

[18]  Christoph H. Gierull,et al.  Improved Space-Based Moving Target Indication via Alternate Transmission and Receiver Switching , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[19]  J.H.G. Ender.  Space-time processing for multichannel synthetic aperture radar , 1999 .