{"title":"多目标粒子滤波检测前跟踪的线性复杂度近似方法","authors":"S. Davey, B. Cheung","doi":"10.1109/DICTA.2015.7371215","DOIUrl":null,"url":null,"abstract":"The particle filter offers the optimal Bayesian filter for track before detect with a single target. However, direct application to the case of multiple targets can be infeasible because the number of particles required grows exponentially. This paper presents a new method for efficiently implementing track before detect for multiple targets using particles. This method is compared with alternative options on a challenging scenario with up to 20 targets.","PeriodicalId":214897,"journal":{"name":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Linear Complexity Approximate Method for Multi-Target Particle Filter Track before Detect\",\"authors\":\"S. Davey, B. Cheung\",\"doi\":\"10.1109/DICTA.2015.7371215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The particle filter offers the optimal Bayesian filter for track before detect with a single target. However, direct application to the case of multiple targets can be infeasible because the number of particles required grows exponentially. This paper presents a new method for efficiently implementing track before detect for multiple targets using particles. This method is compared with alternative options on a challenging scenario with up to 20 targets.\",\"PeriodicalId\":214897,\"journal\":{\"name\":\"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DICTA.2015.7371215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DICTA.2015.7371215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Linear Complexity Approximate Method for Multi-Target Particle Filter Track before Detect
The particle filter offers the optimal Bayesian filter for track before detect with a single target. However, direct application to the case of multiple targets can be infeasible because the number of particles required grows exponentially. This paper presents a new method for efficiently implementing track before detect for multiple targets using particles. This method is compared with alternative options on a challenging scenario with up to 20 targets.