Subspace-Based Detection and Localization in Distributed MIMO Radars

TL;DR

This paper introduces a subspace-based method for detecting and localizing multiple targets in distributed MIMO radars with non-ideal waveforms, using a sequential hypothesis testing approach to improve accuracy.

Contribution

It proposes a novel subspace-based detection and localization technique that iteratively identifies multiple targets in distributed MIMO radar systems with non-ideal waveforms.

Findings

Effective detection and localization of multiple targets demonstrated

Sequential hypothesis testing improves target separation

Method handles non-ideal waveform correlations

Abstract

In this paper, we consider a distributed multiple-input multiple-output (MIMO) radar which radiates waveforms with non-ideal cross- and auto-correlation functions and derive a novel subspace-based procedure to detect and localize multiple prospective targets. The proposed solution solves a sequence of composite binary hypothesis testing problems by resorting to the generalized information criterion (GIC); in particular, at each step, it aims to detect and localize one additional target, upon removing the interference caused by the previously-detected targets. An illustrative example is provided.