异步联邦UKF的GNSS/SINS/摄影定位组合导航算法仿真
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摘要
在飞行器进近过程中,为了提高组合系统的导航精度,针对传统联邦滤波器对非线性系统模型易导致滤波发散问题.分析了两种导航方式的优缺点,提出了基于卫星导航/惯性导航/摄影测量(GNSS/SINS/Photogrammetry)的组合导航联邦滤波算法,并推导了系统误差模型.该算法取长补短利用联邦无迹卡尔曼滤波器将GNSS定位和摄影定位、定姿精度高的优势对SINS进行在线误差估计.针对多传感器非等间隔数据采样问题,采用时间与量测更新分离的异步非等间隔联邦滤波算法进行信息融合,并对滤波器结构进行改进以减少算法复杂度.仿真实验证明基于联邦UKF的组合导航系统较传统联邦滤波算法位姿精度有明显的提高,且系统鲁棒性也有一定的增强.
In the process of aircraft landing,in order to improve the accuracy of the integrated system and solve the problem of the traditional federal filter that the nonlinear system is easy to filter divergence.This paper analyze the advantages and disadvantages of the two system,propose a federal filter algorithm of GNSS/SINS/Photogrammetry,and derived the system's error model.This algorithm can assist the SINS with the high precision of GNSS and Photogrammetry,utilizing the federal unscented Kalman filter to estimate errors of SINS.For non-equal interval data problems of multi-sensor,the information fusion of asynchronous non-equal interval federal filter algorithm use time and measurement update separation,and the corresponding improvements of the federal filter structure to reduce the complexity of the algorithm.Computer simulation shows that the position and attitude accuracy based on the UKF-FKF has been significantly improved comparison with traditional federal filter algorithm.
In the process of aircraft landing,in order to improve the accuracy of the integrated system and solve the problem of the traditional federal filter that the nonlinear system is easy to filter divergence.This paper analyze the advantages and disadvantages of the two system,propose a federal filter algorithm of GNSS/SINS/Photogrammetry,and derived the system's error model.This algorithm can assist the SINS with the high precision of GNSS and Photogrammetry,utilizing the federal unscented Kalman filter to estimate errors of SINS.For non-equal interval data problems of multi-sensor,the information fusion of asynchronous non-equal interval federal filter algorithm use time and measurement update separation,and the corresponding improvements of the federal filter structure to reduce the complexity of the algorithm.Computer simulation shows that the position and attitude accuracy based on the UKF-FKF has been significantly improved comparison with traditional federal filter algorithm.
引文
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[2]谢梅林,杨晓许,韩俊峰,等.北斗导航辅助的机载SINS/CNS组合导航系统研究[C]//中国光学工程研究会.新型导航技术及应用研讨会摘要集.北京:中国工程院信息与电子工程学部,2015:95-99.
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[4]刘帅.GPS/INS组合导航算法研究与实现[D].郑州:解放军信息工程大学,2012.
[5]秦永元,张洪钺,汪叔华.卡尔曼滤波与组合导航原理[M].2版.西安:西北工业大学出版社,2012.
[6]冯国虎.单目视觉/惯性组合导航可观性分析与动态滤波算法研究[D].长沙:国防科学技术大学,2012.
[7]于永军,徐锦法,张梁,等.惯导/双目视觉位姿估计算法研究[J].仪器仪表学报,2014,35(10):2170-2176.
[8]李增科,王坚,高井祥,等.自适应联邦滤波器在GPS-INS-Odometer组合导航的应用[J].测绘学报,2016,45(2):157-163.
[9]高沛林.一种在线调节的联邦卡尔曼组合导航方法[J].水下无人系统学报,2017,25(3):174-179.
[10]卞鸿巍,李安,覃方君,等.现代信息融合技术在组合导航中的应用[M].北京:国防工业出版社,2010.
[11]袁克非.组合导航系统多源信息融合关键技术研究[D].哈尔滨:哈尔滨工程大学,2012.
[12]HU G G,GAO S S.ZGONG Y M.Matrix weighted multisensor data fusion for INS/GNSS/CNS integration[J]//Proceedings of the Institution of Mechanical Engineers,Part G:Journal of Aerospace Engineering,2016,230(6):1011-1026.
[13]周卫东,王巧云.基于矢量信息分配的INS/GNSS/CNS组合导航系统[J].哈尔滨工业大学学报,2015,47(4):99-103.