{"title":"基于接收信号强度的迭代最大似然方法的发射器定位","authors":"Sichun Wang, B. Jackson, S. Rajan, F. Patenaude","doi":"10.1109/MILCOM.2013.21","DOIUrl":null,"url":null,"abstract":"Batch-mode maximum likelihood (ML) received signal strength (RSS) emitter geolocation algorithms produce location estimates from a block of data collected over an observation period using either a single sensor or collected at one time instant by multiple spatially dispersed sensors. Due to practical constraints such as processor speed, memory for data storage, time for data transfer and communications bandwidth, batch-mode algorithms can only be implemented in real-time for small data sets. This paper presents an iterative formulation of the likelihood function for the ML RSS geolocation algorithm for real-time implementation with large data sets. Simulation and experimental results are included to validate the proposed formulation.","PeriodicalId":379382,"journal":{"name":"MILCOM 2013 - 2013 IEEE Military Communications Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Received Signal Strength-Based Emitter Geolocation Using an Iterative Maximum Likelihood Approach\",\"authors\":\"Sichun Wang, B. Jackson, S. Rajan, F. Patenaude\",\"doi\":\"10.1109/MILCOM.2013.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Batch-mode maximum likelihood (ML) received signal strength (RSS) emitter geolocation algorithms produce location estimates from a block of data collected over an observation period using either a single sensor or collected at one time instant by multiple spatially dispersed sensors. Due to practical constraints such as processor speed, memory for data storage, time for data transfer and communications bandwidth, batch-mode algorithms can only be implemented in real-time for small data sets. This paper presents an iterative formulation of the likelihood function for the ML RSS geolocation algorithm for real-time implementation with large data sets. Simulation and experimental results are included to validate the proposed formulation.\",\"PeriodicalId\":379382,\"journal\":{\"name\":\"MILCOM 2013 - 2013 IEEE Military Communications Conference\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MILCOM 2013 - 2013 IEEE Military Communications Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MILCOM.2013.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MILCOM 2013 - 2013 IEEE Military Communications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MILCOM.2013.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Received Signal Strength-Based Emitter Geolocation Using an Iterative Maximum Likelihood Approach
Batch-mode maximum likelihood (ML) received signal strength (RSS) emitter geolocation algorithms produce location estimates from a block of data collected over an observation period using either a single sensor or collected at one time instant by multiple spatially dispersed sensors. Due to practical constraints such as processor speed, memory for data storage, time for data transfer and communications bandwidth, batch-mode algorithms can only be implemented in real-time for small data sets. This paper presents an iterative formulation of the likelihood function for the ML RSS geolocation algorithm for real-time implementation with large data sets. Simulation and experimental results are included to validate the proposed formulation.