{"title":"基于传感器性能预测未知目标数量的贝叶斯方法","authors":"K. Bryan, C. Carthel","doi":"10.1109/ICIF.2006.301776","DOIUrl":null,"url":null,"abstract":"Estimating remaining targets after some attempt has been made to detect an overall, unknown number of targets is critical to determining the potential threat associated with these remaining targets. This paper presents a Bayesian approach to calculate the distribution on the number of remaining targets given the sensor performance and the number of targets detected. For a single sensor, a closed form posterior distribution on remaining targets is derived. For multiple sensors, the corresponding posterior distribution is developed. A naive implementation of this calculation is shown to be computationally prohibitive, and an efficient means for performing the calculation is presented","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Bayesian approach to predicting an unknown number of targets based on sensor performance\",\"authors\":\"K. Bryan, C. Carthel\",\"doi\":\"10.1109/ICIF.2006.301776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Estimating remaining targets after some attempt has been made to detect an overall, unknown number of targets is critical to determining the potential threat associated with these remaining targets. This paper presents a Bayesian approach to calculate the distribution on the number of remaining targets given the sensor performance and the number of targets detected. For a single sensor, a closed form posterior distribution on remaining targets is derived. For multiple sensors, the corresponding posterior distribution is developed. A naive implementation of this calculation is shown to be computationally prohibitive, and an efficient means for performing the calculation is presented\",\"PeriodicalId\":248061,\"journal\":{\"name\":\"2006 9th International Conference on Information Fusion\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 9th International Conference on Information Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIF.2006.301776\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 9th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2006.301776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Bayesian approach to predicting an unknown number of targets based on sensor performance
Estimating remaining targets after some attempt has been made to detect an overall, unknown number of targets is critical to determining the potential threat associated with these remaining targets. This paper presents a Bayesian approach to calculate the distribution on the number of remaining targets given the sensor performance and the number of targets detected. For a single sensor, a closed form posterior distribution on remaining targets is derived. For multiple sensors, the corresponding posterior distribution is developed. A naive implementation of this calculation is shown to be computationally prohibitive, and an efficient means for performing the calculation is presented