Separable Approximation for Solving the Sensor Subset Selection Problem

作     者：Ghassemi, Farhad Krishnamurthy, Vikram 

作者机构：Univ British Columbia Dept Elect Engn Vancouver BC V6T 1Z4 Canada Univ British Columbia Sauder Sch Business Operat & Logist Div Vancouver BC V6T 1Z4 Canada 

出 版 物：《IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS》 (IEEE Trans. Aerosp. Electron. Syst.)

年 卷 期：2011年第47卷第1期

页      面：557-568页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0825[工学-航空宇航科学与技术] 

主　　题：PARAMETER estimation DYNAMIC programming GRAPH algorithms DECISION making CLUSTER analysis (Statistics) PERFORMANCE evaluation INFORMATION theory POLYNOMIALS 

摘      要：An algorithm is proposed to solve the sensor subset selection problem. In this problem, a prespecified number of sensors are selected to estimate the value of a parameter such that a metric of estimation accuracy is maximized. The metric is defined as the determinant of the Bayesian Fisher information matrix (B-FIM). It is shown that the metric can be expanded as a homogenous polynomial of decision variables. In the algorithm, a separable approximation of the polynomial is derived based on a graph-theoretic clustering method. To this end, a graph is constructed where the vertices represent the sensors, and the weights on the edges represent the coefficients of the terms in the polynomial. A process known as natural selection in population genetics is utilized to find the dominant sets of the graph. Each dominant set is considered as one cluster. When the separable approximation is obtained, the sensor selection problem is solved by dynamic programming. Numerical results are provided in the context of localization via direction-of-arrival (DOA) measurements to evaluate the performance of the algorithm.