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

2026-05-08

AI Summary

This study addresses the challenge of detecting subspace signals embedded in non-zero-mean clutter by developing adaptive detectors based on the generalized likelihood ratio test (GLRT), Rao, Wald, gradient, and Durbin criteria. Closed-form expressions for the probabilities of false alarm and detection are derived, revealing two key performance degradations compared to the zero-mean case: a reduction of one effective degree of freedom and a signal-to-clutter ratio loss. Theoretical analysis demonstrates that, while the detector structures remain identical to those in zero-mean scenarios, their performance is significantly influenced by the clutter mean. The effectiveness of the proposed approach and its potential for practical radar applications are validated through both simulated and real-world data.

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.

Problem

• subspace signal detection
• nonzero-mean clutter
• adaptive detection
• radar signal processing
• clutter modeling

Innovation

• adaptive detection
• nonzero-mean clutter
• subspace signal
• GLRT
• performance analysis