{"title":"Noise-Resistant Estimation Algorithm for TDOA and FDOA in Passive Emitter Localization","authors":"Zhixin Liu, D. Hu, Yonziun Zhao, Yongsheng Zhao, Rui Wang, Hongzhi Jiang","doi":"10.1109/WOCC.2019.8770683","DOIUrl":null,"url":null,"abstract":"This paper addresses the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation problem in passive emitter localization, involving range migration during the long observation time. A noise-resistant estimation algorithm based on Keystone transform is proposed. This method can efficiently avoid the Doppler ambiguity problem of the conventional approach by constructing the phase compensation without any signal-to-noise ratio loss. Moreover, compared with the ideal maximum likelihood estimator via extensive numerical experiments, the proposed method can achieve comparable estimation performance and have gain a significant reduction to the computational cost. Extensive numerical experiments validate the effectiveness of the proposed algorithm.","PeriodicalId":285172,"journal":{"name":"2019 28th Wireless and Optical Communications Conference (WOCC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 28th Wireless and Optical Communications Conference (WOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOCC.2019.8770683","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper addresses the joint time difference of arrival (TDOA) and frequency difference of arrival (FDOA) estimation problem in passive emitter localization, involving range migration during the long observation time. A noise-resistant estimation algorithm based on Keystone transform is proposed. This method can efficiently avoid the Doppler ambiguity problem of the conventional approach by constructing the phase compensation without any signal-to-noise ratio loss. Moreover, compared with the ideal maximum likelihood estimator via extensive numerical experiments, the proposed method can achieve comparable estimation performance and have gain a significant reduction to the computational cost. Extensive numerical experiments validate the effectiveness of the proposed algorithm.