Distributed MIMO radar using compressive sampling

TL;DR

This paper introduces a distributed MIMO radar system that employs compressive sampling at receive nodes to efficiently estimate target directions with high resolution using fewer samples, suitable for wireless networks.

Contribution

It applies compressive sampling to distributed MIMO radar for DOA estimation, achieving high resolution with fewer samples than traditional methods.

Findings

Achieves superior resolution in DOA estimation.

Uses fewer samples than Nyquist-based methods.

Effective in distributed wireless network scenarios.

Abstract

A distributed MIMO radar is considered, in which the transmit and receive antennas belong to nodes of a small scale wireless network. The transmit waveforms could be uncorrelated, or correlated in order to achieve a desirable beampattern. The concept of compressive sampling is employed at the receive nodes in order to perform direction of arrival (DOA) estimation. According to the theory of compressive sampling, a signal that is sparse in some domain can be recovered based on far fewer samples than required by the Nyquist sampling theorem. The DOAs of targets form a sparse vector in the angle space, and therefore, compressive sampling can be applied for DOA estimation. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than other approaches. This is particularly useful in a distributed scenario, in which the results at each receive node need to be transmitted to a fusion center.