%0 Journal Article
%T Research on Multi-Sensor Feature Fusion Algorithms in Wireless Sensor Networks<br>无线传感网中多传感器特征融合算法研究
%A Cao Hong-bing
%A Wei Jian-ming
%A Liu Hai-tao
%A <br>曹红兵
%A 魏建明
%A 刘海涛
%J 电子与信息学报
%D 2010
%I 
%X A multi-sensor feature fusion algorithm based on improved Local Discriminant Bases (LDB) and Binary Particle Swarm Optimization (BPSO) is proposed in this paper to satisfy the requirement of application on classification of ground targets in wireless sensor networks. LDB is improved by a new discriminant measure using relative differential entropy based on probability density estimation and used to extract the characteristic frequency band of signals. Then an improved and a new BPSO are used for feature fusion respectively. Based on real acoustic and seismic signals of ground targets, experiment results indicate that this method can decrease the classifier number needed, reduce the dimension of features, and improve the performance of classification at a certain extent, so it is practically valuable for application.
%K Wireless sensor networks
%K Feature fusion
%K Local Discriminant Bases (LDB)
%K Binary Particle Swarm Optimization (BPSO)
%K Discriminant measure<br>无线传感器网络
%K 特征融合
%K 局域判别基
%K 二进制粒子群优化
%K 可分性测度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=238234BED82C8D4F16AB540BC6F86A53&yid=140ECF96957D60B2&vid=9971A5E270697F23&iid=CA4FD0336C81A37A&sid=43608FD2E15CD61B&eid=73579BC9CFB2D787&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=1&reference_num=20