TY - JOUR
T1 - Noise enhanced nonparametric detection
AU - Chen, Hao
AU - Varshney, Pramod K.
AU - Kay, Steven
AU - Michels, James H.
N1 - Funding Information:
Manuscript received November 02, 2006; revised June 24, 2008. This work was supported by AFOSR under contract FA9550-06-C-0036. Current version published February 04, 2009. The material in this paper was presented in part at the 40th Annual Conference on Information Sciences and Systems, Princeton, NJ, March 2006. H. Chen and P. K. Varshney are with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA (e-mail: [email protected]; [email protected]). S. Kay is with Department of Electrical and Computer Engineering, University of Rhode Island, Kingston, RI 02881 USA (e-mail: [email protected]). J. H. Michels is with JHM Technologies, Ithaca, NY 14852 USA (e-mail: [email protected]). Communicated by L. Tong, Associate Editor for Detection and Estimation. Color versions of Figures 2–4 in this paper are available online at http://iee-explore.ieee.org. Digital Object Identifier 10.1109/TIT.2008.2009813
Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Hypothesis testing
KW - Noise enhanced detection
KW - Nonparametric detection
UR - http://www.scopus.com/inward/record.url?scp=61349147851&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=61349147851&partnerID=8YFLogxK
U2 - 10.1109/TIT.2008.2009813
DO - 10.1109/TIT.2008.2009813
M3 - Article
AN - SCOPUS:61349147851
SN - 0018-9448
VL - 55
SP - 499
EP - 506
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 2
ER -