Jizhuang Zhao, Longhua Ma, Ming Xu, Feng Liu, Shaohui Huang
{"title":"GPS/SINS的STUPF滤波算法研究","authors":"Jizhuang Zhao, Longhua Ma, Ming Xu, Feng Liu, Shaohui Huang","doi":"10.1109/ICINFA.2016.7832022","DOIUrl":null,"url":null,"abstract":"Considering the filter divergence problem, a STUPF (Strong Tracting Unscented Particle Filter) algorithm which combines STF (Strong Tracting Filter), UKF (Unscented Kalman Filter) and UPF (Unscented Particle Filter)[1] is proposed. The STUPF algorithm improves the tracking performance of UPF filter by introducing the fading factor K to adjust the filter gain to reduce the weight of the old data, improve the weight of the new data. The traditional re-sampling algorithm can solve the problem of degradation, it is easy to cause the particle depletion; Despite less particle depletion, the extended particle filter algorithm EKF is very weak to track the mutation state; Strong tracking particle filter algorithm STF can improve the tracking ability of the mutation state, but it can't improve the particle degradation. In this paper, the STUPF algorithm is a good solution to solve these problems, and the simulation results also verify the effectiveness of the STUPF algorithm.","PeriodicalId":389619,"journal":{"name":"2016 IEEE International Conference on Information and Automation (ICIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research of STUPF filter algorithm on GPS/SINS\",\"authors\":\"Jizhuang Zhao, Longhua Ma, Ming Xu, Feng Liu, Shaohui Huang\",\"doi\":\"10.1109/ICINFA.2016.7832022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering the filter divergence problem, a STUPF (Strong Tracting Unscented Particle Filter) algorithm which combines STF (Strong Tracting Filter), UKF (Unscented Kalman Filter) and UPF (Unscented Particle Filter)[1] is proposed. The STUPF algorithm improves the tracking performance of UPF filter by introducing the fading factor K to adjust the filter gain to reduce the weight of the old data, improve the weight of the new data. The traditional re-sampling algorithm can solve the problem of degradation, it is easy to cause the particle depletion; Despite less particle depletion, the extended particle filter algorithm EKF is very weak to track the mutation state; Strong tracking particle filter algorithm STF can improve the tracking ability of the mutation state, but it can't improve the particle degradation. In this paper, the STUPF algorithm is a good solution to solve these problems, and the simulation results also verify the effectiveness of the STUPF algorithm.\",\"PeriodicalId\":389619,\"journal\":{\"name\":\"2016 IEEE International Conference on Information and Automation (ICIA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Conference on Information and Automation (ICIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINFA.2016.7832022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Information and Automation (ICIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2016.7832022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Considering the filter divergence problem, a STUPF (Strong Tracting Unscented Particle Filter) algorithm which combines STF (Strong Tracting Filter), UKF (Unscented Kalman Filter) and UPF (Unscented Particle Filter)[1] is proposed. The STUPF algorithm improves the tracking performance of UPF filter by introducing the fading factor K to adjust the filter gain to reduce the weight of the old data, improve the weight of the new data. The traditional re-sampling algorithm can solve the problem of degradation, it is easy to cause the particle depletion; Despite less particle depletion, the extended particle filter algorithm EKF is very weak to track the mutation state; Strong tracking particle filter algorithm STF can improve the tracking ability of the mutation state, but it can't improve the particle degradation. In this paper, the STUPF algorithm is a good solution to solve these problems, and the simulation results also verify the effectiveness of the STUPF algorithm.