Range-doppler radar target detection using denoising within the compressive sensing framework

Compressive sensing (CS) idea enables the reconstruction of a sparse signal from a small set of measurements. CS approach has applications in many practical areas. One of the areas is radar systems. In this article, the radar ambiguity function is denoised within the CS framework. A new denoising method on the projection onto the epigraph set of the convex function is also developed for this purpose. This approach is compared to the other CS reconstruction algorithms. Experimental results are presented1.

[1]  A. Enis Çetin,et al.  Denosing Using Wavelets and Projections onto the L1-Ball , 2014, ArXiv.

[2]  Nelly Pustelnik,et al.  Epigraphical Projection and Proximal Tools for Solving Constrained Convex Optimization Problems: Part I , 2012, ArXiv.

[3]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[4]  A. Enis Çetin,et al.  Projections onto convex sets (POCS) based optimization by lifting , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[5]  Volkan Cevher,et al.  Filtered Variation method for denoising and sparse signal processing , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[7]  Joachim H. G. Ender,et al.  On compressive sensing applied to radar , 2010, Signal Process..

[8]  A. Enis Çetin,et al.  Denoising using projections onto the epigraph set of convex cost functions , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[9]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

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

[11]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[12]  F. Colone,et al.  A Multistage Processing Algorithm for Disturbance Removal and Target Detection in Passive Bistatic Radar , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[13]  D. Donoho,et al.  Basis pursuit , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[14]  Ali Cafer Gürbüz,et al.  A robust compressive sensing based technique for reconstruction of sparse radar scenes , 2014, Digit. Signal Process..

[15]  Shengli Zhou,et al.  Signal Processing for Passive Radar Using OFDM Waveforms , 2010, IEEE Journal of Selected Topics in Signal Processing.

[16]  Shengli Zhou,et al.  Application of compressive sensing to sparse channel estimation , 2010, IEEE Communications Magazine.

[17]  H. Schwarzlander,et al.  Ambiguity function for a bistatic radar , 1992, [1992] Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis.

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

[19]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.