RIP analysis of the measurement matrix for compressive sensing-based MIMO radars

This paper considers range-angle-Doppler estimation in collocated, compressive sensing-based MIMO (CS-MIMO) radars with arbitrary array configuration. In the literature, the effectiveness of CS-MIMO radars has been studied mostly via simulations. Although there exist some theoretical results for MIMO radars with linear arrays, those cannot be easily extended to arbitrary array configurations. This paper analyzes the restricted isometry property (RIP) of the measurement matrix. The RIP conditions involve, among other quantities, the number of transmit and receive antennas. A scheme is proposed that selects the subset of receive antennas with the smallest cardinality that meet the RIP conditions.

[1]  Ieee Staff 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM) , 2014 .

[2]  Athina Petropulu,et al.  Performance guarantees for distributed MIMO radar based on sparse sensing , 2014, 2014 IEEE Radar Conference.

[3]  Benjamin Friedlander,et al.  Analysis of Sparse MIMO Radar , 2012, ArXiv.

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

[5]  Robert D. Nowak,et al.  Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation , 2010, IEEE Transactions on Information Theory.

[6]  Thomas Strohmer,et al.  Remote sensing via $\ell_1$ minimization , 2012 .

[7]  Yonina C. Eldar,et al.  Spatial Compressive Sensing for MIMO Radar , 2013, IEEE Transactions on Signal Processing.

[8]  H. Vincent Poor,et al.  CSSF MIMO RADAR: Compressive-Sensing and Step-Frequency Based MIMO Radar , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[9]  P.P. Vaidyanathan,et al.  Compressed sensing in MIMO radar , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[10]  H. Vincent Poor,et al.  MIMO Radar Using Compressive Sampling , 2009, IEEE Journal of Selected Topics in Signal Processing.