{"title":"Multi-sensor GIW-PHD filter for multiple extended target tracking","authors":"Peng Li, Jinlong Yang, H. Ge, Huanqing Zhang","doi":"10.1109/CCDC.2015.7161802","DOIUrl":null,"url":null,"abstract":"Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has proven to be a promising algorithm for multiple extended target tracking with shape estimation. However, as far as I know, this method only can be used in the single sensor tracking system, which cannot obtain the accurate state estimates for the complex tracking scenario. To solve this problem, we propose a multi-sensor GIW-PHD method by using the multiple sensor infusion technique, which is suitable to the multi-sensor tracking system for multiple extended target tracking. First, a novel measurement model of the extended target is constructed for multi-sensor in three-dimensional scenario, and then the fusion formulas of state update are derived. Simulation results show that the proposed algorithm has a better performance than that of the conventional GIW-PHD with a single sensor.","PeriodicalId":273292,"journal":{"name":"The 27th Chinese Control and Decision Conference (2015 CCDC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 27th Chinese Control and Decision Conference (2015 CCDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2015.7161802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Gaussian inverse Wishart probability hypothesis density (GIW-PHD) filter has proven to be a promising algorithm for multiple extended target tracking with shape estimation. However, as far as I know, this method only can be used in the single sensor tracking system, which cannot obtain the accurate state estimates for the complex tracking scenario. To solve this problem, we propose a multi-sensor GIW-PHD method by using the multiple sensor infusion technique, which is suitable to the multi-sensor tracking system for multiple extended target tracking. First, a novel measurement model of the extended target is constructed for multi-sensor in three-dimensional scenario, and then the fusion formulas of state update are derived. Simulation results show that the proposed algorithm has a better performance than that of the conventional GIW-PHD with a single sensor.
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