MIMO-MC Radar: A MIMO Radar Approach Based on Matrix Completion

TL;DR

This paper introduces MIMO-MC radar, a novel approach that uses matrix completion techniques to recover low-rank data matrices in MIMO radar systems, enabling high-resolution target detection with less data and no grid discretization.

Contribution

It proposes a new MIMO radar method leveraging matrix completion to recover full data matrices from partial samples, improving resolution and reducing data requirements without grid discretization.

Findings

Matrix at the fusion center is low-rank under certain conditions.

MIMO-MC radar achieves high resolution with fewer samples.

Simulation results demonstrate effective target estimation.

Abstract

In a typical MIMO radar scenario, transmit nodes transmit orthogonal waveforms, while each receive node performs matched filtering with the known set of transmit waveforms, and forwards the results to the fusion center. Based on the data it receives from multiple antennas, the fusion center formulates a matrix, which, in conjunction with standard array processing schemes, such as MUSIC, leads to target detection and parameter estimation. In MIMO radars with compressive sensing (MIMO-CS), the data matrix is formulated by each receive node forwarding a small number of compressively obtained samples. In this paper, it is shown that under certain conditions, in both sampling cases, the data matrix at the fusion center 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 that 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 received signal 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 the recently proposed MIMO-CS radars, 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 linear uniform array configuration, and its target estimation performance is demonstrated via simulations.