DocumentCode :
1150951
Title :
Noise Enhanced Nonparametric Detection
Author :
Chen, Hao ; Varshney, Pramod K. ; Kay, Steven ; Michels, James H.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., Syracuse, NY
Volume :
55
Issue :
2
fYear :
2009
Firstpage :
499
Lastpage :
506
Abstract :
This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.
Keywords :
learning (artificial intelligence); noise; signal detection; statistical distributions; Wilcoxon detector; additive noise distribution; dead-zone limiter detector; learning algorithm; noise enhanced nonparametric detection; sign detector; Additive noise; Detectors; Gaussian noise; Nonlinear systems; Signal detection; Signal processing; Signal processing algorithms; Signal to noise ratio; Strontium; Testing; Hypothesis testing; noise enhanced detection; nonparametric detection;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2008.2009813
Filename :
4777627
Link To Document :
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