Paper Info
Title
Persymmetric Adaptive Detectors in Homogeneous and Partially Homogeneous Environments
Abstract
This paper addresses the problem of detecting a signal in partially homogeneous and homogeneous environments. In partially homogeneous environments, i.e., both the test data and training data share the same noise covariance matrix structure up to an unknown scaling factor, a persymmetric adaptive coherence estimator (Per-ACE) detector is proposed. By exploiting the persymmetric structure of the covariance matrix, the Per-ACE can reduce training data requirements. Furthermore, the expressions for the probabilities of false alarm and detection are derived along with the distribution of the loss factor $\beta$. In homogeneous environments, a persymmetric adaptive matched filter (Per-AMF) detector has been presented. However, its probability of detection has not been obtained yet. Thus, we derive the expression for the probability of detection. For both the Per-ACE and Per-AMF, numerical results of these proposed expressions are confirmed with those of Monte Carlo trials. In addition, simulation results show that the proposed Per-ACE outperforms the conventional ACE in training-limited scenarios.
Year
DOI
Venue
2014
10.1109/TSP.2013.2288087
IEEE Transactions on Signal Processing
Keywords
Field
DocType
adaptive signal detection,probabilities of false alarm and detection,adaptive coherence estimator,adaptive matched filter,persymmetry,monte carlo methods,adaptive filters,matched filters
Monte Carlo method,False alarm,Detection theory,Control theory,Algorithm,Adaptive filter,Constant false alarm rate,Matched filter,Covariance matrix,Statistics,Mathematics,Estimator
Journal
Volume
Issue
ISSN
62
2
1053-587X
Citations 
PageRank 
References 
16
0.66
19
Authors
5
Name
Order
Citations
PageRank
Yongchan Gao1567.41
Guisheng Liao2996126.36
Shengqi Zhu335326.46
Xuepan Zhang4866.57
Dong Yang511618.09