Low Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion

Fayazur Rahaman, Mohammad and Khan, Mohammed Zafar Ali (2017) Low Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion. IEEE Signal Processing Letters. p. 1. ISSN 1070-9908 (In Press)

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Abstract

The design of the optimal non-randomized hard decision fusion rule under Neyman-Pearson (NP) criterion is known to be exponential in complexity. In this letter, we formulate a more generalized version of this problem called “Generalized Decision Fusion Problem (GDFP)” and relate it to the classical 0-1 Knapsack Problem. Consequently we show that the GDFP has a worst case polynomial time solution. Numerical results are presented to verify the effectiveness of the proposed solution.

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IITH Creators:	IITH Creators ORCiD Khan, Mohammed Zafar Ali UNSPECIFIED
Item Type:	Article
Uncontrolled Keywords:	Fusion, Likelihood Ratio, Monotonic, Neyman-Pearson, Bayesian, Knapsack, Cognitive Radio, Multi-threshold
Subjects:	Electrical Engineering
Divisions:	Department of Electrical Engineering
Depositing User:	Team Library
Date Deposited:	06 Nov 2017 04:28
Last Modified:	06 Nov 2017 04:28
URI:	http://raiithold.iith.ac.in/id/eprint/3650
Publisher URL:	https://doi.org/10.1109/LSP.2017.2766245
Related URLs:	Google Scholar

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