Compressive Sensing for MIMO Radar

TL;DR

This paper explores the application of compressive sensing in distributed MIMO radar systems to achieve high-resolution direction-of-arrival estimation with fewer samples, enabling efficient data transmission and processing.

Contribution

It introduces a compressive sampling approach for DOA estimation in distributed MIMO radar, improving resolution while reducing sampling requirements.

Findings

Achieves high-resolution DOA estimation with fewer samples.

Reduces data transmission in distributed radar networks.

Demonstrates effectiveness through theoretical analysis and simulations.

Abstract

Multiple-input multiple-output (MIMO) radar systems have been shown to achieve superior resolution as compared to traditional radar systems with the same number of transmit and receive antennas. This paper considers a distributed MIMO radar scenario, in which each transmit element is a node in a wireless network, and investigates the use of compressive sampling for 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 DOA 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 for further processing.