J. Jing, Guo Jing, Luo Peng Fei, Liu Sheng, Sun Zhong Kong
{"title":"Multisensor multiple-attribute data association","authors":"J. Jing, Guo Jing, Luo Peng Fei, Liu Sheng, Sun Zhong Kong","doi":"10.1109/ICR.1996.574474","DOIUrl":null,"url":null,"abstract":"A multisensor system can provide a variety of target information (including dynamic parameters and attribute parameters). Target dynamic parameters are regarded as a kind of target kinematic attribute. A probability assignment method is explained in two cases both of process noise and measurement noise being Gaussis statistics and of being non-Gaussis statistics. A multisensor multiple-attribute data association method is presented based on Dempster and Shafer (D-S) evidence theory, and this approach is illustrated by simulations involving multisensor multiple targets in a dense clutter environment. Comparison with the NN (nearest-neighbour) method which only uses target dynamic parameters shows that the approach has an improved tracking accuracy and resolved correlation ambiguity.","PeriodicalId":144063,"journal":{"name":"Proceedings of International Radar Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of International Radar Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICR.1996.574474","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
A multisensor system can provide a variety of target information (including dynamic parameters and attribute parameters). Target dynamic parameters are regarded as a kind of target kinematic attribute. A probability assignment method is explained in two cases both of process noise and measurement noise being Gaussis statistics and of being non-Gaussis statistics. A multisensor multiple-attribute data association method is presented based on Dempster and Shafer (D-S) evidence theory, and this approach is illustrated by simulations involving multisensor multiple targets in a dense clutter environment. Comparison with the NN (nearest-neighbour) method which only uses target dynamic parameters shows that the approach has an improved tracking accuracy and resolved correlation ambiguity.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
