{"title":"基于神经网络的机载捷联惯导系统误差估计","authors":"Rui Song, Xiyuan Chen","doi":"10.1109/METROAEROSPACE.2017.7999564","DOIUrl":null,"url":null,"abstract":"Strapdown Inertial Navigation System play an important role in many different kinds of applications. As the accuracy of which is deeply influenced by the sensors precision, so the system error propagation should be addressed. Based on the airborne vehicle characteristics, a method based on neural network is proposed to estimate the attitude, velocity and position error of system. The simulation experiment results validate the algorithm in estimate some kind of system errors is better than the traditional method based on Kalman filter model.","PeriodicalId":229414,"journal":{"name":"2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Error estimation of airborne strapdown inertial navigation system based on neural network\",\"authors\":\"Rui Song, Xiyuan Chen\",\"doi\":\"10.1109/METROAEROSPACE.2017.7999564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Strapdown Inertial Navigation System play an important role in many different kinds of applications. As the accuracy of which is deeply influenced by the sensors precision, so the system error propagation should be addressed. Based on the airborne vehicle characteristics, a method based on neural network is proposed to estimate the attitude, velocity and position error of system. The simulation experiment results validate the algorithm in estimate some kind of system errors is better than the traditional method based on Kalman filter model.\",\"PeriodicalId\":229414,\"journal\":{\"name\":\"2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/METROAEROSPACE.2017.7999564\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/METROAEROSPACE.2017.7999564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Error estimation of airborne strapdown inertial navigation system based on neural network
Strapdown Inertial Navigation System play an important role in many different kinds of applications. As the accuracy of which is deeply influenced by the sensors precision, so the system error propagation should be addressed. Based on the airborne vehicle characteristics, a method based on neural network is proposed to estimate the attitude, velocity and position error of system. The simulation experiment results validate the algorithm in estimate some kind of system errors is better than the traditional method based on Kalman filter model.
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