Suboptimum Low Complexity Joint Multi-target Detection and Localization for Noncoherent MIMO Radar with Widely Separated Antennas

TL;DR

This paper introduces low-complexity, robust algorithms for joint detection and localization of multiple targets in noncoherent MIMO radar with widely separated antennas, effectively reducing computational complexity while maintaining accuracy.

Contribution

It proposes two suboptimal algorithms that simplify the joint detection and localization process, eliminating the need for prior target number knowledge and multi-hypothesis testing.

Findings

Algorithms accurately detect and localize multiple targets.

Performance is maintained despite reduced complexity.

Effective even when targets share range bins.

Abstract

In this paper, the problems of simultaneously detecting and localizing multiple targets are considered for noncoherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming a prior knowledge of target number, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching parameters of interest in a high dimensional space. However, the complexity of this method increases exponentially with the number G of targets.Besides, without the prior information of the number of targets, a multi-hypothesis testing strategy to determine the number of targets is required, which further complicates this method. Therefore, we split the joint maximization into G disjoint optimization problems by clearing the interference from previously declared targets. In this way, we derive two fast and robust suboptimal solutions which allow trading performance for a much lower implementation complexity which is almost independent of the number of targets. In addition, the multi-hypothesis testing is no longer required when target number is unknown. Simulation results show the proposed algorithms can correctly detect and accurately localize multiple targets even when targets share common range bins in some paths.