{"title":"GNSS pseudorange error density tracking using Dirichlet Process Mixture","authors":"N. Viandier, J. Marais, A. Rabaoui, E. Duflos","doi":"10.1109/ICIF.2010.5711829","DOIUrl":null,"url":null,"abstract":"In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This article aims to show how the Dirich-let Process Mixture can be employed to track the observation density on-line. This sequential estimation solution is adapted when the noise is non-stationary. The approach will be tested under a simulation scenario with multiple propagation conditions. Then, this density modeling will be used in Rao-Blackwellised Particle Filter.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 13th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2010.5711829","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This article aims to show how the Dirich-let Process Mixture can be employed to track the observation density on-line. This sequential estimation solution is adapted when the noise is non-stationary. The approach will be tested under a simulation scenario with multiple propagation conditions. Then, this density modeling will be used in Rao-Blackwellised Particle Filter.