Adaptive Radar Detection of a Subspace Signal Embedded in Subspace Structured plus Gaussian Interference Via Invariance

TL;DR

This paper introduces an invariant-based adaptive radar detection method for identifying subspace signals amidst Gaussian clutter and structured pulsed jammers, providing a theoretical framework and performance analysis.

Contribution

It develops a new invariant detection framework using the Principle of Invariance for complex interference scenarios in radar signal processing.

Findings

Derivation of a maximal invariant statistic for the problem

Design of practically implementable invariant decision rules

Analytic performance expressions for the proposed detectors

Abstract

This paper deals with adaptive radar detection of a subspace signal competing with two sources of interference. The former is Gaussian with unknown covariance matrix and accounts for the joint presence of clutter plus thermal noise. The latter is structured as a subspace signal and models coherent pulsed jammers impinging on the radar antenna. The problem is solved via the Principle of Invariance which is based on the identification of a suitable group of transformations leaving the considered hypothesis testing problem invariant. A maximal invariant statistic, which completely characterizes the class of invariant decision rules and significantly compresses the original data domain, as well as its statistical characterization are determined. Thus, the existence of the optimum invariant detector is addressed together with the design of practically implementable invariant decision rules. At the analysis stage, the performance of some receivers belonging to the new invariant class is established through the use of analytic expressions.