{"title":"基于多目标粒子PHD滤波的图像观测多目标检测前跟踪","authors":"Ran Zhu, Yunli Long, Zhichao Sha, W. An","doi":"10.1109/PIERS.2017.8262281","DOIUrl":null,"url":null,"abstract":"In order to deal with more complicated situations such as closely spaced objects and target crossings, we propose a recursive multitarget TBD algorithm for image observations based on multi-object particle PHD (MOP-PHD) filter. Instead of sampling from the single target PHD intensity, multi-object set particle sampling is utilized in the approximation of predicted multi-object density. Update of the multi-object state incorporates the multi-object set likelihood function corresponding to a more general observation model to accommodate the overlapping illumination of closely spaced point targets. Each multi-object set particle contains random number of possible single target states, and thus combined with the generalized observation model, the effect of multi-object states can be taken into account simultaneously during the multi-object measurement update procedure. Based on the standard Sequential Monte Carlo PHD (SMC-PHD) filter, multi-object particle PHD filter for image observations is developed and evaluated. Simulation results demonstrate that the proposed method can achieve more accurate estimation without the restriction of non-overlapping assumption, especially when the moving targets become closely spaced.","PeriodicalId":387984,"journal":{"name":"2017 Progress In Electromagnetics Research Symposium - Spring (PIERS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Multitarget track-before-detect from image observations based on multi-object particle PHD filter\",\"authors\":\"Ran Zhu, Yunli Long, Zhichao Sha, W. An\",\"doi\":\"10.1109/PIERS.2017.8262281\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to deal with more complicated situations such as closely spaced objects and target crossings, we propose a recursive multitarget TBD algorithm for image observations based on multi-object particle PHD (MOP-PHD) filter. Instead of sampling from the single target PHD intensity, multi-object set particle sampling is utilized in the approximation of predicted multi-object density. Update of the multi-object state incorporates the multi-object set likelihood function corresponding to a more general observation model to accommodate the overlapping illumination of closely spaced point targets. Each multi-object set particle contains random number of possible single target states, and thus combined with the generalized observation model, the effect of multi-object states can be taken into account simultaneously during the multi-object measurement update procedure. Based on the standard Sequential Monte Carlo PHD (SMC-PHD) filter, multi-object particle PHD filter for image observations is developed and evaluated. Simulation results demonstrate that the proposed method can achieve more accurate estimation without the restriction of non-overlapping assumption, especially when the moving targets become closely spaced.\",\"PeriodicalId\":387984,\"journal\":{\"name\":\"2017 Progress In Electromagnetics Research Symposium - Spring (PIERS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Progress In Electromagnetics Research Symposium - Spring (PIERS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PIERS.2017.8262281\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Progress In Electromagnetics Research Symposium - Spring (PIERS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PIERS.2017.8262281","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multitarget track-before-detect from image observations based on multi-object particle PHD filter
In order to deal with more complicated situations such as closely spaced objects and target crossings, we propose a recursive multitarget TBD algorithm for image observations based on multi-object particle PHD (MOP-PHD) filter. Instead of sampling from the single target PHD intensity, multi-object set particle sampling is utilized in the approximation of predicted multi-object density. Update of the multi-object state incorporates the multi-object set likelihood function corresponding to a more general observation model to accommodate the overlapping illumination of closely spaced point targets. Each multi-object set particle contains random number of possible single target states, and thus combined with the generalized observation model, the effect of multi-object states can be taken into account simultaneously during the multi-object measurement update procedure. Based on the standard Sequential Monte Carlo PHD (SMC-PHD) filter, multi-object particle PHD filter for image observations is developed and evaluated. Simulation results demonstrate that the proposed method can achieve more accurate estimation without the restriction of non-overlapping assumption, especially when the moving targets become closely spaced.