Adaptive Subspace Signal Detection and Performance Analysis in Nonzero-Mean Clutter

TL;DR

This paper develops adaptive subspace detectors for radar signal detection in nonzero-mean clutter, deriving their performance metrics and validating effectiveness with simulations and real data.

Contribution

It introduces new adaptive detectors based on classical tests for nonzero-mean clutter and analyzes their performance differences from zero-mean scenarios.

Findings

Detectors based on GLRT, Rao, and Wald are structurally consistent with zero-mean clutter detectors.

Derived explicit expressions for detection probability and false alarm rate.

Identified two major performance losses: reduced degrees of freedom and signal-to-clutter ratio.

Abstract

To solve the problem of detecting subspace signals in nonzero-mean clutter, we propose adaptive detectors, based on the strategies of generalized likelihood ratio test (GLRT), Rao test, Wald test, gradient test, and Durbin test. The results show that the detectors based on GLRT, Rao and Wald are structurally consistent with the subspace detectors in zero-means clutter. The analytic expressions for the probability of detection (PD) and probability of false alarm (PFA) of each detector are derived, and two major performance differences in the nonzero-mean clutter scenario are revealed. One is the loss of degree of freedom (DOF), which is reduced by 1 compared with the zero-mean clutter scenario. The second is the loss of signal-to-clutter (SCR) ratio. Simulation and measured data verify the effectiveness of the proposed detectors and demonstrate their practical value in real-world radar systems.