Target estimation in colocated MIMO radar via matrix completion

We consider a colocated MIMO radar scenario, in which the receive antennas forward their measurements to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas sample the target returns at Nyquist rate, and assuming that there are more receive antennas than targets, the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%.

[1]  Emmanuel J. Candès,et al.  The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.

[2]  A. Robert Calderbank,et al.  Sensitivity to Basis Mismatch in Compressed Sensing , 2011, IEEE Trans. Signal Process..

[3]  Xin Wang,et al.  Low-rank matrix completion for array signal processing , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[4]  P. P. Vaidyanathan,et al.  MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions , 2008, IEEE Transactions on Signal Processing.

[5]  Volkan Cevher,et al.  Distributed bearing estimation via matrix completion , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Nikos D. Sidiropoulos,et al.  Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar , 2010, IEEE Transactions on Signal Processing.

[7]  Thomas Strohmer,et al.  High-Resolution Radar via Compressed Sensing , 2008, IEEE Transactions on Signal Processing.

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

[9]  Jian Li,et al.  MIMO Radar with Colocated Antennas , 2007, IEEE Signal Processing Magazine.

[10]  Emmanuel J. Candès,et al.  Matrix Completion With Noise , 2009, Proceedings of the IEEE.

[11]  Emmanuel J. Candès,et al.  Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..