【作者】 常琎； 屈重君； 连晓棠； 战兴群；

【Author】 Jin Chang;Zhongjun Qu;Xiaotang Lian;Xingqun Zhan;AVIC Xi’an Flight Automatic Control Research Institute;School of Aeronautics and Astronautics, Shanghai Jiao Tong University;

【机构】 航空工业西安自控所； 上海交通大学航空航天学院；

【摘要】 卫星导航固有的脆弱性使得卫星信号极易受到来自空间段、地面段以及用户段的干扰。这些干扰会产生不同类型的量测误差进而影响组合导航的服务性能。在已有的量测误差类型中,由于缓变误差最难被及时探测,因而被视为卫星导航服务过程中的一大隐患。为了降低缓变误差对卫星/惯性紧组合导航的影响、提升组合导航的服务性能,本文提出了一种基于接收机自主完好性外推(Autonomous Integrity Monitoring Extrapolation,AIME)的鲁棒卡尔曼滤波算法(AIME Based Robust Kalman Filter,ABRKF)。该算法利用AIME可以较快识别缓变误差的特性,有效的解决了鲁棒卡尔曼滤波(Robust Kalman Filter,RKF)对缓变误差不敏感的缺点。同时,通过设置鲁棒权重向量,ABRKF调整了滤波更新过程中的量测噪声,从而有效的减轻了缓变误差对组合导航系统影响。仿真结果证明:相比于RKF,本文提出的ABRKF算法可以有效抑制量测信息中的缓变误差,使紧组合导航的精度得到有效提升。更多还原

【Abstract】 Due to vulnerability, the signal of satellite navigation is susceptible to interference from space segment, ground segment and user segment. Different types of measurement errors caused by these interferences can severely affect the service performance of satellite navigation. Among the existing types of measurement errors, Slowly Growing Error(SGE) is regarded as a major hidden danger of the service of satellite navigation, since it is difficult to be detected by navigation system. To reduce the adverse impact of SGE in satellite/inertial tightly coupled navigation and improve the performance of integrated navigation,Autonomous Integrity Monitoring Extrapolation Based Robust Kalman Filter(ABRKF) is proposed in this paper. By utilizing AIME, ABRKF algorithm is able to detect the SGE quickly and avoid the disadvantage that Robust Kalman Filter(RKF) is not sensitive to SGE. Moreover, by setting the Robust Factor Vector(RFV), ABRKF adjusts the measurement noise covariance matrix in the function of filter updating, so as to effectively reduce the adverse impact of SGE in integrated navigation system. Comparing with RKF,simulation results show that proposed ABRKF algorithm can significantly mitigate the adverse effects of SGE and improve the accuracy of satellite/inertial tightly coupled navigation.更多还原