CFAR Adaptive Matched Detector for Target Detection in Non-Gaussian Noise With Inverse Gamma Texture
Applications
2017-05-15 v1 Methodology
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).
Keywords
Cite
@article{arxiv.1705.04557,
title = {CFAR Adaptive Matched Detector for Target Detection in Non-Gaussian Noise With Inverse Gamma Texture},
author = {Shiwen Lei and Andreas Jakobsson and Zhiqin Zhao},
journal= {arXiv preprint arXiv:1705.04557},
year = {2017}
}