{"title":"一种改进的线性跳跃马尔可夫系统高斯混合概率假设密度滤波器用于机动目标跟踪","authors":"Zhang Shicang, Li Jianxun, Wu Liangbin","doi":"10.1109/ICIEA.2012.6361021","DOIUrl":null,"url":null,"abstract":"An improvement approach to the linear Gaussian jump Markov system (LGJMS) Gaussian Mixture probability hypothesis density (GM-PHD) filter is designed for multiple maneuvering targets tracking. This method, which called mixture LGJMS GM-PHD (MLGJMS GM-PHD) filter, is based on the theory of generalized psuedo Bayes of the first order after the update step of LGJMS GM-PHD. Compared with the existing LGJMS GM-PHD filter, simulation results show that the designed filter weights over the original one.","PeriodicalId":220747,"journal":{"name":"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An improvement to the linear jump Markov system Gaussian mixture probability hypothesis density filter for maneuvering target tracking\",\"authors\":\"Zhang Shicang, Li Jianxun, Wu Liangbin\",\"doi\":\"10.1109/ICIEA.2012.6361021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An improvement approach to the linear Gaussian jump Markov system (LGJMS) Gaussian Mixture probability hypothesis density (GM-PHD) filter is designed for multiple maneuvering targets tracking. This method, which called mixture LGJMS GM-PHD (MLGJMS GM-PHD) filter, is based on the theory of generalized psuedo Bayes of the first order after the update step of LGJMS GM-PHD. Compared with the existing LGJMS GM-PHD filter, simulation results show that the designed filter weights over the original one.\",\"PeriodicalId\":220747,\"journal\":{\"name\":\"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"volume\":\"90 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIEA.2012.6361021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIEA.2012.6361021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improvement to the linear jump Markov system Gaussian mixture probability hypothesis density filter for maneuvering target tracking
An improvement approach to the linear Gaussian jump Markov system (LGJMS) Gaussian Mixture probability hypothesis density (GM-PHD) filter is designed for multiple maneuvering targets tracking. This method, which called mixture LGJMS GM-PHD (MLGJMS GM-PHD) filter, is based on the theory of generalized psuedo Bayes of the first order after the update step of LGJMS GM-PHD. Compared with the existing LGJMS GM-PHD filter, simulation results show that the designed filter weights over the original one.
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