Joint Optimization of Dimension Assignment and Compression in Distributed Estimation Fusion
 报告题目：Joint Optimization of Dimension Assignment and Compression in Distributed Estimation Fusion报告人：宋恩彬报告时间：2019/7/4，9:00-11:00 am报告地点：实验十五楼207会议室欢迎各位老师和同学参加，谢谢！摘要：This work studies linear distributed estimation of an unknown random parameter vector in a bandwidth-constrained multisensor network. To meet the bandwidth limitations, each sensor converts its observation into a low-dimensional datum via a suitable linear transformation. Then, the fusion center estimates the parameter vector by linearly combining all the received low-dimensional data, aiming at minimizing the estimation mean square error. The main purpose of this paper is to jointly determine the compression dimension of each sensor (referred to as dimension assignment) and design the corresponding compression matrix when the total compression dimensions is limited. Such a joint design problem can be formulated as a rank-constrained optimization problem and it is shown to be NP-hard for the first time. In addition, successive quadratic upper-bound minimization (SQUM), SQUMblock coordinate descent (SQUM-BCD) and nuclear norm regularization (NNR) methods are developed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the SQUM method satisfies the Karush-Kuhn-Tucker conditions of the rank-constrained optimization problem, and the Phase II algorithm of the SQUM-BCD and NNR methods (both are two-phase algorithms and have the same Phase II algorithm) guarantees convergence at least to a stationary point. Numerical experiments illustrate the advantages of the proposed methods compared with the existing method. 报告人简介：宋恩彬，教授，博士生导师。2007年于四川大学数学学院获得博士学位并留校工作；2014年7月至今在四川大学数学学院任教授。曾获得2009年全国百篇优秀博士论文提奖和2010年四川省科学技术进步奖一等奖。主要是在从事信息融合，估计与决策，传感器网络，信号处理，非线性优化与大规模优化理论在大数据中的应用等方面的基础研究。 