On Sparse Methods for Array Signal Processing in the Presence of Interference

We analyze the performance of several algorithms designed to solve the inverse sparse problem when they are applied to array signal processing. Specifically we study the error on the estimation of the complex envelope and the direction of arrival of signals of interest in the presence of interference sources using a uniform linear array. In particular, we compare the performance of the Enhanced Sparse Bayesian Learning (ESBL) algorithm against different algorithms tailored to this scenario. Since the former exploits interference information to diminish its unwanted effects, we find that it provides a reasonable tradeoff between runtime and estimation error.

[1]  David L. Donoho,et al.  Sparse Solution Of Underdetermined Linear Equations By Stagewise Orthogonal Matching Pursuit , 2006 .

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

[3]  Bhaskar D. Rao,et al.  Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.

[4]  Bhaskar D. Rao,et al.  Sparse Bayesian learning for basis selection , 2004, IEEE Transactions on Signal Processing.

[5]  Bhaskar D. Rao,et al.  An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem , 2007, IEEE Transactions on Signal Processing.

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

[7]  Carlos H. Muravchik,et al.  Enhanced Sparse Bayesian Learning via Statistical Thresholding for Signals in Structured Noise , 2013, IEEE Transactions on Signal Processing.

[8]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[9]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[10]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[11]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .

[12]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[13]  Bhaskar D. Rao,et al.  Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm , 1997, IEEE Trans. Signal Process..

[14]  Louis L. Scharf,et al.  Signal processing applications of oblique projection operators , 1994, IEEE Trans. Signal Process..

[15]  Jie Chen,et al.  Theoretical Results on Sparse Representations of Multiple-Measurement Vectors , 2006, IEEE Transactions on Signal Processing.

[16]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[17]  Dean Zhao,et al.  A Sparse Representation Method for DOA Estimation With Unknown Mutual Coupling , 2012, IEEE Antennas and Wireless Propagation Letters.

[18]  P. Rocca,et al.  Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies , 2013, IEEE Transactions on Antennas and Propagation.

[19]  Arye Nehorai,et al.  Polarimetric Detection of Targets in Heavy Inhomogeneous Clutter , 2008, IEEE Transactions on Signal Processing.

[20]  Carlos H. Muravchik,et al.  Electromagnetic source imaging for sparse cortical activation patterns , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.