{"title":"A localization method based on TDOA self-calibrating of rotating short baselineon single observer","authors":"Fuhe Ma, M. Zhang, F. Guo","doi":"10.1109/SIPROCESS.2016.7888286","DOIUrl":null,"url":null,"abstract":"A non-cooperative source localization method utilizing the time difference of arrival (TDOA) of a rotating short baseline on a single observer is proposed in this paper. The localization bias caused by constant TDOA measurement bias is analyzed. It can be proved that this localization bias can be auto-eliminated when the sum of baseline orientation vectors that correspond with all TDOA measurements equals to zero. Otherwise, in order to mitigate the localization bias caused by TDOA measurement bias, a recursive nonlinear least squares(NLS) based self-calibrating localization method is proposed where the TDOA measurement bias and source position are estimated jointly. The Cramér-Rao lower bound (CRLB) of the proposed method is also analyzed. Simulation results show that the performance of the proposed method can be close to the CRLB under moderate Gaussian TDOA measurement noise.","PeriodicalId":142802,"journal":{"name":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Conference on Signal and Image Processing (ICSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIPROCESS.2016.7888286","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
A non-cooperative source localization method utilizing the time difference of arrival (TDOA) of a rotating short baseline on a single observer is proposed in this paper. The localization bias caused by constant TDOA measurement bias is analyzed. It can be proved that this localization bias can be auto-eliminated when the sum of baseline orientation vectors that correspond with all TDOA measurements equals to zero. Otherwise, in order to mitigate the localization bias caused by TDOA measurement bias, a recursive nonlinear least squares(NLS) based self-calibrating localization method is proposed where the TDOA measurement bias and source position are estimated jointly. The Cramér-Rao lower bound (CRLB) of the proposed method is also analyzed. Simulation results show that the performance of the proposed method can be close to the CRLB under moderate Gaussian TDOA measurement noise.