Compressive SAR raw data with principal component analysis

The theory of synthetic aperture radar (SAR) signal model is briefly introduced which is constructed with a series of echo signals in range direction. The procedure of principal component analysis (PCA) is presented which is used as transformation basis to sparsify the signals. The joint compressive sensing (CS) and PCA algorithm is devised to realize SAR raw data compressive measurement. A SAR raw data for a point target is simulated and used to verify the performance of the joint CS and PCA algorithm. The numerical experimental results demonstrate that the PCA method has good sparse performance and the joint CS and PCA algorithm is possible to online compressively measure the SAR raw data.

[1]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[2]  T. Blumensath,et al.  Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[3]  Zhang Qun,et al.  SAR Imagery Compressing and Reconstruction Method Based on Compressed Sensing , 2012 .

[4]  Ludger Pruente.  Application of Compressed Sensing to SAR/GMTI-Data , 2010 .

[5]  Fang Liu,et al.  Compressive Sensing SAR Image Reconstruction Based on Bayesian Framework and Evolutionary Computation , 2011, IEEE Transactions on Image Processing.

[6]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[7]  Jörg Widmer,et al.  Data Acquisition through Joint Compressive Sensing and Principal Component Analysis , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[8]  Michele Zorzi,et al.  A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks , 2009, 2009 International Conference on Ultra Modern Telecommunications & Workshops.

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

[10]  Yinghui Quan,et al.  SAR imaging of multiple ships based on compressed sensing , 2009, 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar.

[11]  Michael B. Wakin,et al.  An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition] , 2008 .

[12]  Lawrence Carin,et al.  Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

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

[14]  Mehrdad Soumekh,et al.  Synthetic Aperture Radar Signal Processing with MATLAB Algorithms , 1999 .

[15]  Bernie Mulgrew,et al.  Compressed sensing based compression of SAR raw data , 2009 .

[16]  Shunjun Wei,et al.  SPARSE RECONSTRUCTION FOR SAR IMAGING BASED ON COMPRESSED SENSING , 2010 .

[17]  Amir Averbuch,et al.  Adaptive Compressed Image Sensing Using Dictionaries , 2012, SIAM J. Imaging Sci..