{"title":"多传感器交通映射过滤器","authors":"R. Streit","doi":"10.1109/SDF.2012.6327906","DOIUrl":null,"url":null,"abstract":"A traffic intensity filter is derived using a probability generating functional approach. Traffic filters estimate, or map, the mean rate at which different regions of state space generate target detection opportunities in a field of distributed sensors. They are Bayesian filters that incorporate sensor measurement likelihood functions and target detection capabilities. Traffic maps contribute to situational awareness for heterogeneous sensor fields. They are practical for applications with large numbers of sensors because their computational complexity is linear in the numbers of sensors and measurements.","PeriodicalId":212723,"journal":{"name":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Multisensor traffic mapping filters\",\"authors\":\"R. Streit\",\"doi\":\"10.1109/SDF.2012.6327906\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A traffic intensity filter is derived using a probability generating functional approach. Traffic filters estimate, or map, the mean rate at which different regions of state space generate target detection opportunities in a field of distributed sensors. They are Bayesian filters that incorporate sensor measurement likelihood functions and target detection capabilities. Traffic maps contribute to situational awareness for heterogeneous sensor fields. They are practical for applications with large numbers of sensors because their computational complexity is linear in the numbers of sensors and measurements.\",\"PeriodicalId\":212723,\"journal\":{\"name\":\"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)\",\"volume\":\"188 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SDF.2012.6327906\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SDF.2012.6327906","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A traffic intensity filter is derived using a probability generating functional approach. Traffic filters estimate, or map, the mean rate at which different regions of state space generate target detection opportunities in a field of distributed sensors. They are Bayesian filters that incorporate sensor measurement likelihood functions and target detection capabilities. Traffic maps contribute to situational awareness for heterogeneous sensor fields. They are practical for applications with large numbers of sensors because their computational complexity is linear in the numbers of sensors and measurements.
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