MIMO Radar Using Compressive Sampling

TL;DR

This paper introduces a MIMO radar system that employs compressive sampling and sparse recovery techniques to accurately detect target angles and Doppler shifts with fewer samples, enhancing resolution and power efficiency.

Contribution

It presents a novel MIMO radar approach using compressive sampling and l1-optimization to improve resolution and reduce sampling requirements compared to traditional methods.

Findings

Achieves high-resolution target detection with fewer samples.

Demonstrates robustness against jamming and noise.

Reduces power consumption during data transmission.

Abstract

A MIMO radar system is proposed for obtaining angle and Doppler information on potential targets. Transmitters and receivers are nodes of a small scale wireless network and are assumed to be randomly scattered on a disk. The transmit nodes transmit uncorrelated waveforms. Each receive node applies compressive sampling to the received signal to obtain a small number of samples, which the node subsequently forwards to a fusion center. Assuming that the targets are sparsely located in the angle- Doppler space, based on the samples forwarded by the receive nodes the fusion center formulates an l1-optimization problem, the solution of which yields target angle and Doppler information. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than required by other approaches. This implies power savings during the communication phase between the receive nodes and the fusion center. Performance in the presence of a jammer is analyzed for the case of slowly moving targets. Issues related to forming the basis matrix that spans the angle-Doppler space, and for selecting a grid for that space are discussed. Extensive simulation results are provided to demonstrate the performance of the proposed approach at difference jammer and noise levels.