{"title":"鲁棒自适应SDRE滤波器及其在SINS/SAR集成中的应用","authors":"Zhaohui Gao, Bingbing Gao, Dejun Mu, Shesheng Gao","doi":"10.1109/ICISCE.2016.300","DOIUrl":null,"url":null,"abstract":"This paper presents a new robust adaptive filter based on the state dependent Riccati equation (SDRE) technique for SINS/SAR (strap-down inertial navigation system/synthetic aperture radar) integrated navigation. This method adopts the state dependent coefficient (SDC) form to convert nonlinear system model into linear system for avoiding the errors caused by the traditional numerical linearization process. It also adopts the concepts of robust estimation and adaptive factor to construct reasonably the discriminant statistics and adaptive factor for resisting the disturbances of singular observations and kinematic model error. Experiment results and comparison analysis demonstrate that the proposed filtering method can not only effectively resist disturbances from nonlinear system state noise and observation noise, but it also can achieve higher accuracy than the extended Kalman filter and SDRE filter.","PeriodicalId":6882,"journal":{"name":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Robust Adaptive SDRE Filter and Its Application to SINS/SAR Integration\",\"authors\":\"Zhaohui Gao, Bingbing Gao, Dejun Mu, Shesheng Gao\",\"doi\":\"10.1109/ICISCE.2016.300\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new robust adaptive filter based on the state dependent Riccati equation (SDRE) technique for SINS/SAR (strap-down inertial navigation system/synthetic aperture radar) integrated navigation. This method adopts the state dependent coefficient (SDC) form to convert nonlinear system model into linear system for avoiding the errors caused by the traditional numerical linearization process. It also adopts the concepts of robust estimation and adaptive factor to construct reasonably the discriminant statistics and adaptive factor for resisting the disturbances of singular observations and kinematic model error. Experiment results and comparison analysis demonstrate that the proposed filtering method can not only effectively resist disturbances from nonlinear system state noise and observation noise, but it also can achieve higher accuracy than the extended Kalman filter and SDRE filter.\",\"PeriodicalId\":6882,\"journal\":{\"name\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISCE.2016.300\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Information Science and Control Engineering (ICISCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISCE.2016.300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust Adaptive SDRE Filter and Its Application to SINS/SAR Integration
This paper presents a new robust adaptive filter based on the state dependent Riccati equation (SDRE) technique for SINS/SAR (strap-down inertial navigation system/synthetic aperture radar) integrated navigation. This method adopts the state dependent coefficient (SDC) form to convert nonlinear system model into linear system for avoiding the errors caused by the traditional numerical linearization process. It also adopts the concepts of robust estimation and adaptive factor to construct reasonably the discriminant statistics and adaptive factor for resisting the disturbances of singular observations and kinematic model error. Experiment results and comparison analysis demonstrate that the proposed filtering method can not only effectively resist disturbances from nonlinear system state noise and observation noise, but it also can achieve higher accuracy than the extended Kalman filter and SDRE filter.