Optimal linear fusion for distributed detection via semidefinite programming

Consider the problem of signal detection via multiple distributed noisy sensors. We propose a linear decision fusion rule to combine the local statistics from individual sensors into a global statistic for binary hypothesis testing. The objective is to maximize the probability of detection subject to an upper limit on the probability of false alarm. We employ a divide-and-conquer strategy to divide the decision optimization problem into two subproblems, each of which is a nonconvex program with a quadratic constraint. Through a judicious reformulation and by employing a special matrix decomposition technique, we show that the two nonconvex subproblems can be solved by semidefinite programs in a globally optimal fashion. Hence, we can obtain the optimal linear fusion rule for the distributed detection problem. Compared with the likelihood-ratio test approach, the optimized linear fusion rule can achieve comparable detection performance with considerable design flexibility and reduced c...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Zhi Quan, Wing-Kin Ma, Shuguang Cui, Ali H. Sayed
• Keywords: Artificial Intelligence, Detection, Fusion Rule, Linear Fusion Rule, TSP 2010