车载组合导航技术应用研究
摘要
车辆导航技术无论在军事领域还是民用领域都非常重要。目前组合导航系统广泛应用到车载系统中。
捷联惯性导航系统(SINS)是一种自主式导航系统,可以连续提供多种导航信息,短时间内运行的精度较高,然而其定位误差随时间积累,长时间精度较差,必须要有外界辅助信息进行组合导航。全球定位系统(GPS)长期误差特性较好,但是短期精度不如纯惯导。因此以SINS/GPS为组合方式的组合导航系统能够取长补短,是一种理想的组合方式。虽然惯导和GPS组合具有众多的优越性,在战时会有较大的风险,惯导和测速仪组合可以实现完全自主导航,因此研究惯导和外部测速设备的组合导航方式同样具有重要的现实意义。
本论文主要研究车载组合导航系统,主要针对捷联惯导/GPS组合导航和捷联惯导/测速仪组合导航两种组合导航方法进行深入的研究。论文第二章简要的介绍了捷联惯性导航系统(SINS)的基本原理、全球定位系统(GPS)基础知识以及构建实际系统所采用的测速仪的相关内容。第三章对实际系统中需要采用的最优估计方法—卡尔曼滤波的原理进行了详细的推导和描述,给出了卡尔曼滤波的滤波递推方程。第四章介绍了SINS/GPS组合导航系统,基于某型号车载导航系统的实际需求,对该组合导航的状态方程和测量方程进行的深入的推导,构造了位置组合的卡尔曼滤波模型,在实际系统上进行了大量的跑车试验,文中给出了某次试验的试验结果。第五章介绍了SINS/测速仪组合导航系统,为提高系统的精度,系统考虑了测速仪标定因素的误差,详细的给出了组合导航系统的状态方程和测量方程,并在构造的实际系统上进行了大量的实际跑车试验,文中给出了某次试验结果。两种组合导航试验初步验证了系统设计的正确性,后期将更进一步的对系统的精度进行验证。
Vehicle navigation technology is very important both in the field of military and civilian areas. At present, combinated navigation system widely used in the Vehicle navigation.
Inertial Navigation System (SINS) is an autonomous navigation system, which can provide many kinds of information .It has high precision in a short period of time , but its location error accumulated as time go on, poor accuracy for a long time. Global Positioning System (GPS) long-term error characteristics is better, but not the accuracy of short-term error. Therefore, SINS / GPS combination navigation system can combine strongpoint to each other, is an ideal combination. Alrough it has many good point, but in real time, it can be easily disturbed. SINS/ speed sensor navigation can work without any outside information, so the research of SINS/speed sensor navigation is also very important.
The main content of thesis is research on vehicle navigation system, and to study the multi-sensor navigation system of filtering technology. The second chapter is devoted to the thesis component of the vehicle navigation system .introduce three types of navigation systems - Inertial Navigation System (SINS), Global Positioning System (GPS), and the basic information of speed sensor. The third chapter is devoted to vehicle navigation in the filtering technology - Kalman filter,and gives out the equations of kalman filter. The forth chapter introduced the SINS/GPS navigation system, contracture the structure of the Kalman filter model. There are four combination modes as follow: location combination, velocity combination, velocity and location combination, velocity and acceleration combination. Construct the Kalman filter model and give out one of the actual tests. The fifth chapter introduced the SINS/speed sensor combination Navigation system, in order to gain the best navigation output, in this system we have considered the factor error of the speed sensor. In this paper the states equations were gave out, and gives the Kalman filter design. On the base of actual system, we have made lots of actual test, in this paper one of the tests was gave out.
捷联惯性导航系统(SINS)是一种自主式导航系统,可以连续提供多种导航信息,短时间内运行的精度较高,然而其定位误差随时间积累,长时间精度较差,必须要有外界辅助信息进行组合导航。全球定位系统(GPS)长期误差特性较好,但是短期精度不如纯惯导。因此以SINS/GPS为组合方式的组合导航系统能够取长补短,是一种理想的组合方式。虽然惯导和GPS组合具有众多的优越性,在战时会有较大的风险,惯导和测速仪组合可以实现完全自主导航,因此研究惯导和外部测速设备的组合导航方式同样具有重要的现实意义。
本论文主要研究车载组合导航系统,主要针对捷联惯导/GPS组合导航和捷联惯导/测速仪组合导航两种组合导航方法进行深入的研究。论文第二章简要的介绍了捷联惯性导航系统(SINS)的基本原理、全球定位系统(GPS)基础知识以及构建实际系统所采用的测速仪的相关内容。第三章对实际系统中需要采用的最优估计方法—卡尔曼滤波的原理进行了详细的推导和描述,给出了卡尔曼滤波的滤波递推方程。第四章介绍了SINS/GPS组合导航系统,基于某型号车载导航系统的实际需求,对该组合导航的状态方程和测量方程进行的深入的推导,构造了位置组合的卡尔曼滤波模型,在实际系统上进行了大量的跑车试验,文中给出了某次试验的试验结果。第五章介绍了SINS/测速仪组合导航系统,为提高系统的精度,系统考虑了测速仪标定因素的误差,详细的给出了组合导航系统的状态方程和测量方程,并在构造的实际系统上进行了大量的实际跑车试验,文中给出了某次试验结果。两种组合导航试验初步验证了系统设计的正确性,后期将更进一步的对系统的精度进行验证。
Vehicle navigation technology is very important both in the field of military and civilian areas. At present, combinated navigation system widely used in the Vehicle navigation.
Inertial Navigation System (SINS) is an autonomous navigation system, which can provide many kinds of information .It has high precision in a short period of time , but its location error accumulated as time go on, poor accuracy for a long time. Global Positioning System (GPS) long-term error characteristics is better, but not the accuracy of short-term error. Therefore, SINS / GPS combination navigation system can combine strongpoint to each other, is an ideal combination. Alrough it has many good point, but in real time, it can be easily disturbed. SINS/ speed sensor navigation can work without any outside information, so the research of SINS/speed sensor navigation is also very important.
The main content of thesis is research on vehicle navigation system, and to study the multi-sensor navigation system of filtering technology. The second chapter is devoted to the thesis component of the vehicle navigation system .introduce three types of navigation systems - Inertial Navigation System (SINS), Global Positioning System (GPS), and the basic information of speed sensor. The third chapter is devoted to vehicle navigation in the filtering technology - Kalman filter,and gives out the equations of kalman filter. The forth chapter introduced the SINS/GPS navigation system, contracture the structure of the Kalman filter model. There are four combination modes as follow: location combination, velocity combination, velocity and location combination, velocity and acceleration combination. Construct the Kalman filter model and give out one of the actual tests. The fifth chapter introduced the SINS/speed sensor combination Navigation system, in order to gain the best navigation output, in this system we have considered the factor error of the speed sensor. In this paper the states equations were gave out, and gives the Kalman filter design. On the base of actual system, we have made lots of actual test, in this paper one of the tests was gave out.
引文
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[3] D. Goshen-Meskin and I. Y. Bar-Itzhack. Observability analysis of piece-wise constant systems-part I: Theory. IEEE Trans. Aerosp. Electron. Systems. Vol. 28, Oct. 1992. 1056–1067P
[4] Yeon Fuh Jiang, Yu Ping Lin. Error Estimation of INS Ground Alignment Through Observability Analysis. IEEE Transactions on Aerospace and Electronic Systems, Vol. 28, No. 1, January 1992, 92-97P
[5]董绪荣,张守信,华仲春.GPS/INS组合导航定位及其应用长沙:国防科技大学出版社. 1998.
[6]刘基于,李征航,王跃虎等.全球定位系统原理及应用.北京:测绘出版社,1993
[7]李涛,吴美平,江明,庹洲慧.激光陀螺SINS/GPS组合导航车载试验系统.国防科技大学学报,2003,25(3):104-108
[8] Roya Rahbari, Barrie W Leach, Jeremy Dillon, Clarence W. De Silva. Adaptive tuning of a Kalman filter using the fuzzy integral for an intelligent navigation system. IEEE International Symposium on Intelligent Control - Proceedings, 2002, 252-257P
[9] Steven R. Swanson. A Fuzzy Navigational State Estimator for GPS/INS Integration. IEEE PLANS, Position Location and Navigation Symposium, 1998, 541-548P
[10]J.Z. Sasiadek, Q. Wang, M.B. Zeremba. Fuzzy Adaptive Kalman Filtering for INS/GPS Data Fusion. IEEE International Symposium on Intelligent Control - Proceedings, 2000, 181-186P
[11]Zhaohu, Song; Zhe, Chen; Yuezu, Fan. Self-adjustable fuzzy management system of integrated INS/GPS/TAN. IECON Proceedings (Industrial Electronics Conference), v 3, 1996, 1467-1471P
[12]Antonio Tiano, Antonio Zirilli, Fausto Pizzocchero. Application of interval and fuzzy techniques to integrated navigation systems. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, v 1, 2001, 13-18P
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