{"title":"基于权值优化的改进粒子滤波算法","authors":"Juntao Zhu, Xiaolong Wang, Qiansheng Fang","doi":"10.1109/ISCC-C.2013.140","DOIUrl":null,"url":null,"abstract":"Particle filter algorithm is to achieve recursive Bayesian filter through the simulation method of non-parameter Monte Carlo, It based on sequential importance sampling, and can not avoid particle degeneration problem, a way to overcome the particle degradation is re-sampling, However sample impoverishment will appear in the process of re-sampling, This paper proposes an improved particle filter method based on optimized weight. To some extent, the method solves the particle impoverishment problem, According to simulation results, we can confirm that the improved particle filter algorithm proposed in the paper can effectively improve the estimation precision of particle filter algorithm.","PeriodicalId":313511,"journal":{"name":"2013 International Conference on Information Science and Cloud Computing Companion","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"The Improved Particle Filter Algorithm Based on Weight Optimization\",\"authors\":\"Juntao Zhu, Xiaolong Wang, Qiansheng Fang\",\"doi\":\"10.1109/ISCC-C.2013.140\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Particle filter algorithm is to achieve recursive Bayesian filter through the simulation method of non-parameter Monte Carlo, It based on sequential importance sampling, and can not avoid particle degeneration problem, a way to overcome the particle degradation is re-sampling, However sample impoverishment will appear in the process of re-sampling, This paper proposes an improved particle filter method based on optimized weight. To some extent, the method solves the particle impoverishment problem, According to simulation results, we can confirm that the improved particle filter algorithm proposed in the paper can effectively improve the estimation precision of particle filter algorithm.\",\"PeriodicalId\":313511,\"journal\":{\"name\":\"2013 International Conference on Information Science and Cloud Computing Companion\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Information Science and Cloud Computing Companion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC-C.2013.140\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Information Science and Cloud Computing Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC-C.2013.140","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Improved Particle Filter Algorithm Based on Weight Optimization
Particle filter algorithm is to achieve recursive Bayesian filter through the simulation method of non-parameter Monte Carlo, It based on sequential importance sampling, and can not avoid particle degeneration problem, a way to overcome the particle degradation is re-sampling, However sample impoverishment will appear in the process of re-sampling, This paper proposes an improved particle filter method based on optimized weight. To some extent, the method solves the particle impoverishment problem, According to simulation results, we can confirm that the improved particle filter algorithm proposed in the paper can effectively improve the estimation precision of particle filter algorithm.