Scale-Invariant GLRT in Stochastic Partially Homogeneous Environments
نویسندگان
چکیده
The generalized likelihood ratio test (GLRT) for a stochastic partially homogeneous model is proposed for adaptive signal detection. The stochastic partially homogeneous model generalizes the standard partially homogeneous model by treating the disturbance covariance matrix as a random matrix. Specifically, it assumes that, R, the disturbance covariance matrix of training signals, is a random matrix with some a priori information, while R0, the disturbance covariance matrix of the test signal, is equal to R, i.e., R0 = R, where is an unknown scaling factor. The proposed GLRT uses a colored loading of the a priori knowledge and the sample covariance matrix for the adaptive signal detection. Numerical examples show that the GLRT is invariant to scaling factor and outperforms the adaptive coherence estimator (ACE), which is the GLRT for the standard partially homogeneous case, in the stochastic partially homogeneous environment. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2011 201 Broadway, Cambridge, Massachusetts 02139
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تاریخ انتشار 2011