{"title":"存在干扰时的航迹融合","authors":"M. Krieg, D. Gray","doi":"10.1109/ISSPA.1996.615710","DOIUrl":null,"url":null,"abstract":"The problems of poor performance and track loss in the presence of interfering targets when using a Kalman filter to fuse tracks in multiple sensor systems are addressed. Two variants of the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for multisensor tracking are presented to address these problems. A comparison of performance is made between these two algorithms, and a heuristic algorithm based on the normalised innovations from each sensor.","PeriodicalId":359344,"journal":{"name":"Fourth International Symposium on Signal Processing and Its Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Track Fusion in the Presence of an Interference\",\"authors\":\"M. Krieg, D. Gray\",\"doi\":\"10.1109/ISSPA.1996.615710\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problems of poor performance and track loss in the presence of interfering targets when using a Kalman filter to fuse tracks in multiple sensor systems are addressed. Two variants of the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for multisensor tracking are presented to address these problems. A comparison of performance is made between these two algorithms, and a heuristic algorithm based on the normalised innovations from each sensor.\",\"PeriodicalId\":359344,\"journal\":{\"name\":\"Fourth International Symposium on Signal Processing and Its Applications\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth International Symposium on Signal Processing and Its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPA.1996.615710\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth International Symposium on Signal Processing and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.1996.615710","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The problems of poor performance and track loss in the presence of interfering targets when using a Kalman filter to fuse tracks in multiple sensor systems are addressed. Two variants of the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for multisensor tracking are presented to address these problems. A comparison of performance is made between these two algorithms, and a heuristic algorithm based on the normalised innovations from each sensor.
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