Abstract
In a typical multiple-input and multiple-output (MIMO) radar scenario, the receive nodes transmit to a fusion center either samples of the target returns, or the results of matched filtering with the transmit waveforms. Based on the data it receives from multiple antennas, the fusion center formulates a matrix, referred to as the data matrix, which, via standard array processing schemes leads to target detection and parameter estimation. In this paper, it is shown that under certain conditions, the data matrix is low rank and thus can be recovered based on knowledge of a small subset of its entries via matrix completion (MC) techniques. Leveraging the low-rank property of the data matrix, we propose a new MIMO radar approach, termed, MIMO-MC radar, in which each receive node either performs matched filtering with a small number of randomly selected dictionary waveforms or obtains sub-Nyquist samples of the target returns at random sampling instants, and forwards the results to a fusion center. Based on the received samples, and with knowledge of the sampling scheme, the fusion center partially fills the data matrix and subsequently applies MC techniques to estimate the full matrix. MIMO-MC radars share the advantages of MIMO radars with compressive sensing, (MIMO-CS), i.e., high resolution with reduced amounts of data, but unlike MIMO-CS radars do not require grid discretization. The MIMO-MC radar concept is illustrated through a uniform linear array configuration, and its target estimation performance is demonstrated via simulations.