Phase adjustment for polarimetric ISAR with compressive sensing

Polarimetric inverse synthetic aperture radar (pol-ISAR) imaging exploits extra scattering information about a target compared with single pol-ISAR. Target description and classification can be improved by incorporating pol-ISAR images. One of the most critical problems in ISAR imaging is accurate motion compensation, specifically for noncooperative targets. Due to the imperfection of coarse motion compensation, phase-adjustment methods are further developed to eliminate the residual phase errors. Unlike taking the contrast or entropy as the measurements in the existing pol-ISAR phase-adjustment algorithms, this paper exploits the sparsity of the scattering centers to correct the phase errors and simultaneously proposes an unambiguous image formation with the sparse aperture signal. The superiority of this algorithm is that the global information of full pol-ISAR images can be properly incorporated to compensate the phase errors and suppress the noise effectively. Experimental results indicate the effectiveness of the proposed method.

[1]  Wolfgang Stefan,et al.  Total variation regularization for linear ill -posed inverse problems: Extensions and applications , 2008 .

[2]  Junfeng Wang,et al.  Minimum-entropy phase adjustment for ISAR , 2004 .

[3]  Adrian Stern,et al.  Compressed Imaging With a Separable Sensing Operator , 2009, IEEE Signal Processing Letters.

[4]  Marco Martorella,et al.  Optimised image autofocusing for polarimetric ISAR , 2006, 2006 14th European Signal Processing Conference.

[5]  Li Xi,et al.  Autofocusing of ISAR images based on entropy minimization , 1999 .

[6]  Thomas Strohmer,et al.  Compressed sensing radar , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Chenwei Zhu,et al.  Stable Recovery of Sparse Signals Via Regularized Minimization , 2008, IEEE Transactions on Information Theory.

[8]  Hyo-Tae Kim,et al.  Two-dimensional ISAR imaging using full polarisation and super-resolution processing techniques , 1998 .

[9]  Charles V. Jakowatz,et al.  Phase gradient autofocus-a robust tool for high resolution SAR phase correction , 1994 .

[10]  Xiaoyong Du,et al.  Sparse Representation Based Autofocusing Technique for ISAR Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Marco Martorella,et al.  Automatic Target Recognition by Means of Polarimetric ISAR Images and Neural Networks , 2009, IEEE Trans. Geosci. Remote. Sens..

[12]  Gang Li,et al.  Adaptive Sparse Recovery by Parametric Weighted L$_{1}$ Minimization for ISAR Imaging of Uniformly Rotating Targets , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  M. Martorella,et al.  Image contrast and entropy based autofocusing for polarimetric ISAR , 2007, 2007 International Waveform Diversity and Design Conference.

[14]  Lu Wang,et al.  An Autofocus Technique for High-Resolution Inverse Synthetic Aperture Radar Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  C. Vogel,et al.  Analysis of bounded variation penalty methods for ill-posed problems , 1994 .

[16]  V. C. Chen,et al.  Simulation of ISAR imaging of moving targets , 2001 .

[17]  Gang Li,et al.  Parametric sparse representation method for ISAR imaging of rotating targets , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[18]  R. Baraniuk,et al.  Compressive Radar Imaging , 2007, 2007 IEEE Radar Conference.

[19]  Richard G. Baraniuk,et al.  Kronecker Compressive Sensing , 2012, IEEE Transactions on Image Processing.

[20]  Qiang Fu,et al.  High-Resolution Fully Polarimetric ISAR Imaging Based on Compressive Sensing , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Mengdao Xing,et al.  Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling , 2009, IEEE Geoscience and Remote Sensing Letters.

[22]  O. Axelsson.  Iterative solution methods , 1995 .

[23]  Zheng Bao,et al.  High-Resolution ISAR Imaging by Exploiting Sparse Apertures , 2012, IEEE Transactions on Antennas and Propagation.

[24]  Junfeng Wang,et al.  Global range alignment for ISAR , 2003 .

[25]  Michael P. Friedlander,et al.  Theoretical and Empirical Results for Recovery From Multiple Measurements , 2009, IEEE Transactions on Information Theory.

[26]  F. Berizzi,et al.  Autofocusing of inverse synthetic aperture radar images using contrast optimization , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[27]  S.A.S. Werness,et al.  Moving target imaging algorithm for SAR data , 1990 .

[28]  Yachao Li,et al.  Resolution Enhancement for Inversed Synthetic Aperture Radar Imaging Under Low SNR via Improved Compressive Sensing , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Chung-ching Chen,et al.  Target-Motion-Induced Radar Imaging , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[30]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[31]  Gang Li,et al.  ISAR 2-D Imaging of Uniformly Rotating Targets via Matching Pursuit , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[32]  Marco Martorella,et al.  Polarimetric ISAR autofocusing , 2008 .