DocumentCode :
2415172
Title :
Correntropy Based Matched Filtering
Author :
Pokharel, Puskal P. ; Agrawal, Rati ; Principe, Jose C.
Author_Institution :
ECE Dept., Florida Univ., Gainesville, FL
fYear :
2005
fDate :
28-28 Sept. 2005
Firstpage :
341
Lastpage :
346
Abstract :
In this paper a non-linear extension to the matched filter is proposed and applied to signal detection. The decision statistic used in this novel method is derived from ideas on kernel-based learning theory and in fact, is a generalization of the correlation statistic used in the matched filter. The optimality of the matched filter is merely based on second order statistics and hence leaves room for improvement, especially when the assumption of Gaussianity is no longer valid. The proposed method incorporates higher order moments in the decision statistic and shows different behavior than the matched filter and improvement in the detection rate for non-Gaussian noise. Moreover, unlike kernel based approaches, this method is still computationally tractable and can easily be implemented in real-time
Keywords :
correlation theory; decision theory; entropy; filtering theory; learning (artificial intelligence); matched filters; signal detection; correlation statistic; correntropy; decision statistic; kernel-based learning theory; matched filtering; nonGaussian noise; second order statistics; signal detection; AWGN; Additive white noise; Computational complexity; Gaussian noise; Higher order statistics; Kernel; Matched filters; Mutual information; Nonlinear filters; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning for Signal Processing, 2005 IEEE Workshop on
Conference_Location :
Mystic, CT
Print_ISBN :
0-7803-9517-4
Type :
conf
DOI :
10.1109/MLSP.2005.1532925
Filename :
1532925
Link To Document :
بازگشت