Compressed-Domain Detection and Estimation for Colocated MIMO Radar

TL;DR

This paper introduces a compressed domain processing method for MIMO radar that significantly reduces sample complexity and improves detection speed by applying multiple levels of data compression and sparse signal techniques.

Contribution

It presents a novel CSP MIMO radar approach with dual compression stages and a hypothesis testing detection method, outperforming traditional CS algorithms.

Findings

Achieves up to 8-fold reduction in sample complexity.

Outperforms traditional compressed sensing algorithms in detection accuracy.

Enables faster target detection with robust clutter suppression.

Abstract

This paper proposes compressed domain signal processing (CSP) multiple input multiple output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an 8-fold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar thus enabling faster target detection. Receiver operating characteristic (ROC) curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based compressed sensing algorithms.