Low-Complexity Optimal Hard Decision Fusion Under the Neyman–Pearson Criterion
Fayazur Rahaman Mohammad, Mohammed Zafar Ali Khan · IEEE Signal Processing Letters · 2017
The design of the optimal nonrandomized hard decision fusion rule under the Neyman-Pearson criterion is known to be exponential in complexity. In this letter, we formulate a more generalized version of this problem called the “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.