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一种改进的低成本车载MIMU/GPS组合导航系统算法 被引量:2

A Better Low-Cost MIMU/GPS Integrated Navigation Algorithm for Land Vehicle
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摘要 针对低成本车载MIMU/GPS组合导航系统中存在的航向角可观测性较弱的问题,建立了一种加入GPS测速所获得的航向角信息的量测方程,增强系统航向角的可观测性,从而解决了低成本车载MIMU/GPS组合导航中的航向角可观测性较弱的问题。同时,为提高实时计算效率,并考虑低精度惯性器件噪声统计特性不易准确获得,采用降阶状态模型,并设计改进型强跟踪卡尔曼滤波与U-D分解相结合的滤波算法来抑制模型不精确造成的滤波发散。跑车实验表明,所设计的方法能够很好适用于低成本车载MIMU/GPS组合导航系统。 Sections 1 though 4 of the full paper explain our algorithm mentioned in the title, which we believe is better than existing ones. Their core consists of: ( 1 ) due to the problem of weak observability of yaw angle in Chi- nese low-cost MIMU/GPS integrated navigation for land vehicle, we put forward a new measurement equation adding the information of yaw angle determined from GPS velocity, which enhances the observability of yaw angle and solves the problem; MINU stands for Miniature Inertial Measurement Unit and GPS stands for Global Positio- ning System; (2) for improving the efficiency of real-time calculations and considering hard-to-obtain statistical properties of low-precision inertial devices, we adopt reduced order state model; (3) our filter algorithm, which combines that of the modified strong tracking Kalman filter with that of the UD decomposition filter, is designed to suppress the filter divergence cause of imprecise model. The experimental results, presented in Figs. 2 through 7 and Table 1, and their analysis show preliminarily that our algorithm is indeed better for the low-cost MIMU/GPS integrated navigation system for land vehicle.
出处 《西北工业大学学报》 EI CAS CSCD 北大核心 2012年第6期952-956,共5页 Journal of Northwestern Polytechnical University
基金 航空科学基金(20090853013 20100853010)资助
关键词 MIMU GPS组合导航 航向角 改进型强跟踪卡尔曼滤波 U-D分解滤波 MINU/GPS integrated navigation yaw angle Kalman filters measurement errors
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  • 1李博文,姚丹亚.低成本车载MEMS惯导导航定位方法[J].中国惯性技术学报,2014,12(6):719-723. 被引量:20
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  • 6Chris D,Vassilis G. An iterative LiDAR DEM-aided algorithm for GNSS positioning in obstructed/rapidly undulating environments[J].{H}Advances in Space Research,2013,(05):865-878.
  • 7Ma Longhua,Guan Bo. SINS/GPS integrated navigation system using an improved particle filter based on state reconstruction[J].Control Engineering and Applied Informatics,2011,(03):58-64.
  • 8Li Wanli,Wu Wenqi,Wang Jinliang. A fast SINS initial alignment scheme for underwater vehicle applica-tions[J].{H}Journal of Navigation,2013,(02):181-198.
  • 9张国龙.基于DSP的SINS/GNSS组合导航系统设计与研究[D]{H}南京:东南大学,2011.
  • 10钱华明,夏全喜,阙兴涛,张强.基于Kalman滤波的MEMS陀螺仪滤波算法[J].哈尔滨工程大学学报,2010,31(9):1217-1221. 被引量:44

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