{"title":"卡尔曼滤波平稳性分析在惯性导航系统误差估计中的应用","authors":"I. Rataichuk, V. Kortunov","doi":"10.1109/MSNMC.2012.6475082","DOIUrl":null,"url":null,"abstract":"Kalman filter has long been used in aircraft integrated navigation systems as the main instrument of estimating navigation errors. Its main advantage over simpler state estimators is the ability to adjust filter's gain according to observable signal instability. But it is possible to use simple observers if filters gain is stationary regardless of aircraft maneuvers. In this paper the model of navigation errors dynamics is considered. This model is used in Kalman filer in integrated system simulation. Frequency domain representation of vehicle dynamics is used in this simulation to investigate filter stationarity. The results show that filter gain is stationary regardless of motion dynamics.","PeriodicalId":394899,"journal":{"name":"2012 2nd International Conference \"Methods and Systems of Navigation and Motion Control\" (MSNMC)","volume":"2004 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Kalman filter stationarity analysis in the problem of INS errors estimation\",\"authors\":\"I. Rataichuk, V. Kortunov\",\"doi\":\"10.1109/MSNMC.2012.6475082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Kalman filter has long been used in aircraft integrated navigation systems as the main instrument of estimating navigation errors. Its main advantage over simpler state estimators is the ability to adjust filter's gain according to observable signal instability. But it is possible to use simple observers if filters gain is stationary regardless of aircraft maneuvers. In this paper the model of navigation errors dynamics is considered. This model is used in Kalman filer in integrated system simulation. Frequency domain representation of vehicle dynamics is used in this simulation to investigate filter stationarity. The results show that filter gain is stationary regardless of motion dynamics.\",\"PeriodicalId\":394899,\"journal\":{\"name\":\"2012 2nd International Conference \\\"Methods and Systems of Navigation and Motion Control\\\" (MSNMC)\",\"volume\":\"2004 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 2nd International Conference \\\"Methods and Systems of Navigation and Motion Control\\\" (MSNMC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSNMC.2012.6475082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 2nd International Conference \"Methods and Systems of Navigation and Motion Control\" (MSNMC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSNMC.2012.6475082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Kalman filter stationarity analysis in the problem of INS errors estimation
Kalman filter has long been used in aircraft integrated navigation systems as the main instrument of estimating navigation errors. Its main advantage over simpler state estimators is the ability to adjust filter's gain according to observable signal instability. But it is possible to use simple observers if filters gain is stationary regardless of aircraft maneuvers. In this paper the model of navigation errors dynamics is considered. This model is used in Kalman filer in integrated system simulation. Frequency domain representation of vehicle dynamics is used in this simulation to investigate filter stationarity. The results show that filter gain is stationary regardless of motion dynamics.