{"title":"利用JMS-GM-PHD滤波器跟踪道路约束下的地面目标","authors":"Jihong Zheng, He He, Longteng Cong","doi":"10.1145/3457682.3457768","DOIUrl":null,"url":null,"abstract":"The probability hypothesis density filter with linear Gaussian jump Markov system multi-target models is an attractive approach to tracking multiple maneuvering targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. However, these models are not precise enough to describe moving targets on road networks in ground target tracking scenario. In this paper, the road map information is integrated into the jump Markov system Gaussian mixture probability hypothesis density (JMS-GM-PHD) filter, and a road-constraint JMS-GM-PHD filter for ground target tracking is proposed. In addition, we then derive the recursive equation of the proposed filter. Simulation results show that the proposed road-constrained JMS-GM-PHD filter is effective in tracking ground moving targets.","PeriodicalId":142045,"journal":{"name":"2021 13th International Conference on Machine Learning and Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Tracking Ground Targets with Road Constraints Using a JMS-GM-PHD Filter\",\"authors\":\"Jihong Zheng, He He, Longteng Cong\",\"doi\":\"10.1145/3457682.3457768\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The probability hypothesis density filter with linear Gaussian jump Markov system multi-target models is an attractive approach to tracking multiple maneuvering targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. However, these models are not precise enough to describe moving targets on road networks in ground target tracking scenario. In this paper, the road map information is integrated into the jump Markov system Gaussian mixture probability hypothesis density (JMS-GM-PHD) filter, and a road-constraint JMS-GM-PHD filter for ground target tracking is proposed. In addition, we then derive the recursive equation of the proposed filter. Simulation results show that the proposed road-constrained JMS-GM-PHD filter is effective in tracking ground moving targets.\",\"PeriodicalId\":142045,\"journal\":{\"name\":\"2021 13th International Conference on Machine Learning and Computing\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 13th International Conference on Machine Learning and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3457682.3457768\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 13th International Conference on Machine Learning and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3457682.3457768","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tracking Ground Targets with Road Constraints Using a JMS-GM-PHD Filter
The probability hypothesis density filter with linear Gaussian jump Markov system multi-target models is an attractive approach to tracking multiple maneuvering targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. However, these models are not precise enough to describe moving targets on road networks in ground target tracking scenario. In this paper, the road map information is integrated into the jump Markov system Gaussian mixture probability hypothesis density (JMS-GM-PHD) filter, and a road-constraint JMS-GM-PHD filter for ground target tracking is proposed. In addition, we then derive the recursive equation of the proposed filter. Simulation results show that the proposed road-constrained JMS-GM-PHD filter is effective in tracking ground moving targets.
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