Impact of covariance mismatched training samples on constant false alarm rate detectors

Abstract

The framework of this paper is that of adaptive detection in Gaussian noise with unknown covariance matrix when the training samples do not share the same covariance matrix as the vector under test. We consider a class of constant false alarm rate detectors which depend on two statistics $(β,\ttilde)$ whose distribution is parameter-free in the case of no mismatch and we analyze the impact of covariance mismatched training samples. More precisely, we provide a statistical representation of these two variables for an arbitrary mismatch. We show that covariance mismatch induces significant variations of the probability of false alarm and we investigate a way to mitigate this effect.

References in corpus (4)

• Adaptive Detection of Point-like Targets in Spectrally Symmetric Interference
• Radar Adaptive Detection Architectures for Heterogeneous Environments
• CFAR Feature Plane: a Novel Framework for the Analysis and Design of Radar Detectors
• Analysis of the SNR loss distribution with covariance mismatched training samples

Cited by in corpus (2)

• On the distributions of some statistics related to adaptive filters trained with $t$-distributed samples
• A short overview of adaptive multichannel filters SNR loss analysis