Measurement Matrix Design for Compressive Sensing Based MIMO Radar

TL;DR

This paper designs optimized measurement matrices for compressive sensing in MIMO radar, improving target detection accuracy by balancing coherence and signal-to-interference ratio, with potential for significant performance gains.

Contribution

It introduces two novel measurement matrix design methods that enhance CS recovery performance in MIMO radar by optimizing coherence and SIR, considering waveform structure.

Findings

Proposed matrices outperform Gaussian random matrices in simulations.

Optimized matrices improve detection accuracy.

Design methods adapt to different waveform structures.

Abstract

In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as measurement matrix. The samples are subsequently forwarded to a fusion center, where an L1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits the target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with many fewer measurements. The measurement matrix is vital for CS recovery performance. This paper considers the design of measurement matrices that achieve an optimality criterion that depends on the coherence of the sensing matrix (CSM) and/or signal-to-interference ratio (SIR). The first approach minimizes a performance penalty that is a linear combination of CSM and the inverse SIR. The second one imposes a structure on the measurement matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve SIR, while maintaining CSM comparable to that of the Gaussian random measurement matrix (GRMM). Simulations indicate that the proposed measurement matrices can improve detection accuracy as compared to a GRMM.