{"title":"Advanced TOA Estimation For Multipath Channels","authors":"R. Chrabieh, Peter Bagnall, S. Sezginer, D. Slock","doi":"10.1109/PLANS46316.2020.9109833","DOIUrl":null,"url":null,"abstract":"In this paper, estimation of time of arrival (TOA) in multipath environments is tackled as it is considered a real limiting factor in terrestrial and GNSS positioning systems. Novel methods are presented for designing novel near-causal filters and TOA-Matched Filters (TOA-MF) that facilitate estimation of the TOA. Near and far multipath are effectively reduced including in NLOS environments. Post application of the filter, a reduced complexity Maximum Likelihood (ML) estimation of the TOA is proposed. Simulations and field results confirm the efficiency of the proposed solution. This opens the door for optimized multi-technology approaches, which allows cost-effective and ultra-low-power solutions for accurate positioning.","PeriodicalId":273568,"journal":{"name":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLANS46316.2020.9109833","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 this paper, estimation of time of arrival (TOA) in multipath environments is tackled as it is considered a real limiting factor in terrestrial and GNSS positioning systems. Novel methods are presented for designing novel near-causal filters and TOA-Matched Filters (TOA-MF) that facilitate estimation of the TOA. Near and far multipath are effectively reduced including in NLOS environments. Post application of the filter, a reduced complexity Maximum Likelihood (ML) estimation of the TOA is proposed. Simulations and field results confirm the efficiency of the proposed solution. This opens the door for optimized multi-technology approaches, which allows cost-effective and ultra-low-power solutions for accurate positioning.