Ahmed W. Ebrahim, I. Arafa, H. Hendy, Y. Elhalwagy
{"title":"高速飞行器导航应用中集成导航系统的增强","authors":"Ahmed W. Ebrahim, I. Arafa, H. Hendy, Y. Elhalwagy","doi":"10.1109/NILES50944.2020.9257982","DOIUrl":null,"url":null,"abstract":"The Inertial Navigation Systems (INS), Global Navigation Satellite System (GNSS) integration becomes very important for high speed flying vehicles as a navigation solution. In this paper, a derivation and modeling of a system model of the integrated system for a 27-states Kalman filter is presented. Sensors (gyroscopes and accelerometers) errors, and GNSS errors are characterized and modeled as well. Results show that some parameters of estimated gyroscopes (gyros) errors such as vertical and east gyro drifts and the estimated east accelerometer bias are not observables. The simulation shows that in the integrated system and the navigation errors in both the INS and GNSS can be estimated with high accuracy. Results analysis proves that the development of state estimation for an Inertial Measuring Unit (IMU) can efficiently supply current motion information (position and attitude states). This efficient information can be used to carry out accurate guidance and control strategies for such a hi-speed flight bodies.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancement of Integrated Navigation System for high-speed flying vehicles' Navigation Applications\",\"authors\":\"Ahmed W. Ebrahim, I. Arafa, H. Hendy, Y. Elhalwagy\",\"doi\":\"10.1109/NILES50944.2020.9257982\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Inertial Navigation Systems (INS), Global Navigation Satellite System (GNSS) integration becomes very important for high speed flying vehicles as a navigation solution. In this paper, a derivation and modeling of a system model of the integrated system for a 27-states Kalman filter is presented. Sensors (gyroscopes and accelerometers) errors, and GNSS errors are characterized and modeled as well. Results show that some parameters of estimated gyroscopes (gyros) errors such as vertical and east gyro drifts and the estimated east accelerometer bias are not observables. The simulation shows that in the integrated system and the navigation errors in both the INS and GNSS can be estimated with high accuracy. Results analysis proves that the development of state estimation for an Inertial Measuring Unit (IMU) can efficiently supply current motion information (position and attitude states). This efficient information can be used to carry out accurate guidance and control strategies for such a hi-speed flight bodies.\",\"PeriodicalId\":253090,\"journal\":{\"name\":\"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NILES50944.2020.9257982\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NILES50944.2020.9257982","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhancement of Integrated Navigation System for high-speed flying vehicles' Navigation Applications
The Inertial Navigation Systems (INS), Global Navigation Satellite System (GNSS) integration becomes very important for high speed flying vehicles as a navigation solution. In this paper, a derivation and modeling of a system model of the integrated system for a 27-states Kalman filter is presented. Sensors (gyroscopes and accelerometers) errors, and GNSS errors are characterized and modeled as well. Results show that some parameters of estimated gyroscopes (gyros) errors such as vertical and east gyro drifts and the estimated east accelerometer bias are not observables. The simulation shows that in the integrated system and the navigation errors in both the INS and GNSS can be estimated with high accuracy. Results analysis proves that the development of state estimation for an Inertial Measuring Unit (IMU) can efficiently supply current motion information (position and attitude states). This efficient information can be used to carry out accurate guidance and control strategies for such a hi-speed flight bodies.