南京邮电大学学报（自然科学版）2018，Vol.38Issue(2)：48-53,59,7.DOI:10.14132/j.cnki.1673-5439.2018.02.008
基于变分贝叶斯推理的多目标无源定位算法
Variational Bayesian inference based multi-target device-free localization algorithm
摘要
Abstract
To improve the accuracy and robust of the multi-target device-free localization,a variational Bayesian inference based multi-target device-free localization algorithm is proposed.Firstly,by establishing a hierarchical prior model,the spatial sparsity of target location vector is induced.Then,the posteriors of the hidden variables in the hierarchical prior model are estimated by using the variational Bayesian inference method.Finally,the positions of multi-targets are estimated according to the posteriors of the estimated target location vector.Simulation results show that the proposed algorithm has better performance on localization accuracy and more robust than the conventional compressive sensing based multi-target device-free localization algorithms.关键词
无线传感器网络/多目标无源定位/压缩感知/变分贝叶斯推理Key words
wireless sensor networks/multi-target device-free localization/compressive sensing (CS)/variational Bayesian inference分类
信息技术与安全科学引用本文复制引用
余东平,何谢,齐扬阳,赖荣煊,袁健..基于变分贝叶斯推理的多目标无源定位算法[J].南京邮电大学学报（自然科学版）,2018,38(2):48-53,59,7.基金项目
国家自然科学基金(61571463,61371124,61472445)资助项目 （61571463,61371124,61472445）
