Noise enhanced nonparametric detection

Authors:
Hao Chen;Pramod K. Varshney;Steven Kay;James H. Michels
Affiliations:
Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI;JHM Technologies, Ithaca, NY
Venue:
IEEE Transactions on Information Theory
Year:
2009

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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.