Nonparametric Decentralized Detection and Sparse Sensor Selection Via Weighted Kernel

—The kernel-based nonparametric approach proposed by Nguyen, Wainwright and Jordan is further investigated for decentralized detection. In contrast to the uniform kernel used in the previous work, a weighted kernel is proposed, where weight parameters serve to selectively incorporate sensors’ information into the fusion center’s decision rule based on quality of sensors’ observations. Furthermore, weight parameters also serve as sensor selection parameters with nonzero parameters corresponding to sensors being selected. By introducing the l1 regularization on weight parameters into the risk minimization framework, sensor selection is jointly performed with decision rules for sensors and the fusion center with the resulting optimal decision rule having only sparse nonzero weight parameters. A gradient projection algorithm and a Gauss-Seidel algorithm are developed to solve the risk minimization problem, which is non-convex, and both algorithms are shown to converge to critical p...

• Type: Journal
• Year: 2016
• Where: TSP
• Authors: Weiguang Wang, Yingbin Liang, Eric P. Xing, Lixin Shen
• Keywords: TSP 2016