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%.Comment: 5 pages, ICASSP 201

Matrix Completion in Colocated MIMO Radar: Recoverability, Bounds & Theoretical Guarantees

It was recently shown that low rank matrix completion theory can be employed for designing new sampling schemes in the context of MIMO radars, which can lead to the reduction of the high volume of data typically required for accurate target detection and estimation. Employing random samplers at each reception antenna, a partially observed version of the received data matrix is formulated at the fusion center, which, under certain conditions, can be recovered using convex optimization. This paper presents the theoretical analysis regarding the performance of matrix completion in colocated MIMO radar systems, exploiting the particular structure of the data matrix. Both Uniform Linear Arrays (ULAs) and arbitrary 2-dimensional arrays are considered for transmission and reception. Especially for the ULA case, under some mild assumptions on the directions of arrival of the targets, it is explicitly shown that the coherence of the data matrix is both asymptotically and approximately optimal with respect to the number of antennas of the arrays involved and further, the data matrix is recoverable using a subset of its entries with minimal cardinality. Sufficient conditions guaranteeing low matrix coherence and consequently satisfactory matrix completion performance are also presented, including the arbitrary 2-dimensional array case.Comment: 19 pages, 7 figures, under review in Transactions on Signal Processing (2013