MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter

TL;DR

This paper investigates the estimation and resolution limits of target angular spacing in MIMO radar under non-Gaussian SIRP clutter, proposing new estimators, bounds, and analytical expressions validated through simulations.

Contribution

It introduces novel iterative maximum likelihood and a posteriori estimators, derives closed-form bounds and resolution limits, and provides analytical expressions for target resolvability under SIRP clutter.

Findings

Proposed estimators outperform existing methods in SIRP clutter.

Derived closed-form CRLBs and ARL expressions match simulation results.

Analytical ARL provides insights into target resolvability in complex clutter environments.

Abstract

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a non-Gaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing between two targets for the MIMO radar under the SIRP clutter. First, we propose an iterative maximum likelihood as well as an iterative maximum a posteriori estimator, for the target's spacing parameter estimation in the SIRP clutter context. Then we derive and compare various Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we address the problem of target resolvability by using the concept of angular resolution limit (ARL), and derive an analytical, closed-form expression of the ARL based on Smith's criterion, between two closely spaced targets in a MIMO radar context under SIRP clutter. For this aim we also obtain the non-matrix, closed-form expressions for each of the CRLBs. Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.