On the performance of a persymmetric adaptive matched filter

• Published in: IEEE transactions on aerospace and electronic systems Vol. 51; no. 4; pp. 2605 - 2614
• Main Authors: Jun Liu, Guolong Cui, Hongbin Li, Himed, Braham
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Colored noise; Computer simulation; Covariance matrices; Covariance matrix; Detectors; False alarms; Fluctuation; Matched filters; Mathematical models; Monte Carlo methods; Probability; Radar detection; Symmetric matrices; Training data
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2015.140633

We examine the adaptive detection problem in the presence of colored noise with an unknown covariance matrix, by exploiting a persymmetric structure in the received signal. The persymmetric adaptive matched filter (PS-AMF) is used to address this problem, which can significantly alleviate the requirement of secondary data. In G. Pailloux et al. "Persymmetric adaptive radar detectors," (2011) the probability of false alarm of the PS-AMF has been obtained in terms of the Gaussian hypergeometric function. In this paper, finite-sum expressions for the probability of false alarm of the PS-AMF are derived, which are more convenient to use in calculating the detection threshold. Moreover, the detection probabilities of the PS-AMF for both nonfluctuating and fluctuating target models are derived. In the fluctuating model, the amplitude of the target echoes is described by a generalized Chi distribution that involves the Rayleigh distribution as a special case. These theoretical results are all confirmed using Monte Carlo (MC) simulations.