Tongyu Ge, R. Tharmarasa, Bernard Lebel, M. Florea, T. Kirubarajan
{"title":"Target Localization and Sensor Synchronization in the Presence of Data Association Uncertainty","authors":"Tongyu Ge, R. Tharmarasa, Bernard Lebel, M. Florea, T. Kirubarajan","doi":"10.23919/fusion43075.2019.9011321","DOIUrl":null,"url":null,"abstract":"In passive sensor networks, sensor registration and data association are two essential processes. Although these two processes affect each other, they are usually addressed separately. In this paper, we propose an algorithm to localize multiple targets and estimate sensor clock biases using time difference of arrival (TDOA) measurements in the presence of data association uncertainty. The problem is formulated as a multidimensional optimization problem, where the objective is to maximize the generalized likelihood of the associated measurements based on target position and sensor clock bias estimates. Computer simulations are carried out to evaluate the performance of the proposed algorithm.","PeriodicalId":348881,"journal":{"name":"2019 22th International Conference on Information Fusion (FUSION)","volume":"1630 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 22th International Conference on Information Fusion (FUSION)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/fusion43075.2019.9011321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In passive sensor networks, sensor registration and data association are two essential processes. Although these two processes affect each other, they are usually addressed separately. In this paper, we propose an algorithm to localize multiple targets and estimate sensor clock biases using time difference of arrival (TDOA) measurements in the presence of data association uncertainty. The problem is formulated as a multidimensional optimization problem, where the objective is to maximize the generalized likelihood of the associated measurements based on target position and sensor clock bias estimates. Computer simulations are carried out to evaluate the performance of the proposed algorithm.