CFAR Adaptive Matched Detector for Target Detection in Non-Gaussian Noise With Inverse Gamma Texture

TL;DR

This paper introduces an adaptive matched detector designed for signals in non-Gaussian noise with inverse gamma texture, demonstrating improved performance and constant false alarm rate properties through analytical validation and simulations.

Contribution

It presents a novel adaptive matched detector tailored for non-Gaussian noise with inverse gamma texture, outperforming existing detectors like 1S-GLRT and ASD.

Findings

Detector maintains constant false alarm rate

Outperforms 1S-GLRT and ASD in simulations

Validated analytically and via Monte Carlo simulations

Abstract

In this paper, we propose an adaptive matched detector of a signal corrupted by a non-Gaussian noise with an inverse gamma texture. The detector is formed using a set of secondary data measurements, and is analytically shown to have a constant false alarm rate. The analytic performance is validated using Monte Carlo simulations, and the proposed detector is shown to offer preferable performance as compared to the related one-step generalized likelihood ratio test (1S-GLRT) and the adaptive subspace detector (ASD).