Reduced complexity optimal hard decision fusion under Neyman-Pearson criterion

It is known that nonrandomized hard decision fusion under Neyman-Pearson criterion is a NP-hard 0 — 1 knapsack problem with exponential complexity in general. Recently, a low complexity dynamic programming based solution was proposed for this. In this paper, we show that the decision fusion problem exhibits semi-monotonic property in a special case. We propose to exploit this property to reduce the dimension of the feasible solution space. Subsequently, we apply dynamic programming to efficiently solve the problem with further reduction in complexity. Numerical results are provided to verify the correctness of the proposed solution.