改进型CKF算法及其在GNSS/INS中的应用  

Improved CKF Algorithm and Its Application in GNSS/INS

作　　者：王永杰[1] 吴峻[1] WANG Yong-Jie;WU Jun(Key Laboratory of Micro Inertial Instrument and Advanced Navigation Technology,Southeast University,Nanjing 210096,China)

机构地区：[1]东南大学微惯性仪表与先进导航技术重点实验室,江苏南京210096

出　　处：《测控技术》2018年第4期112-115,119,共5页Measurement & Control Technology

基　　金：国土资源部公益性行业科研专项经费课题(201411012-02)

摘　　要：针对容积卡尔曼滤波算法(CKF)在高阶非线性系统应用中,局部容积点采样不准确及系统状态突变导致滤波精度降低甚至发散的问题,提出一种改进型CKF(TSCKF)算法。采用新的容积点采样规则提高非线性函数映射准确性,进而提高滤波精度;引入强跟踪滤波(STF)的渐消因子,提升算法在状态突变时的鲁棒性。将此算法应用于GNSS/INS(Global Navigation Satellite System/Inertial Navigation System)紧组合导航系统中,仿真结果表明,该算法能够显著提高导航精度,鲁棒性强,在状态突变情况下依然可以满足导航精度要求。In order to solve the problems that the cubature point sampling was inaccurate and the system state mutation caused the filtering-accuracy decreasing or even diverging by using the cubature Kalman filtering(CKF)algorithm in the high order nonlinear system,a modified CKF(TSCKF)algorithm was proposed.A new cubature point sampling rule was adopted to improve the mapping accuracy of nonlinear function,and then to improve the filtering accuracy.The fading factor of strong tracking filtering(STF)was introduced to improve the robustness of the algorithm in mutation state.This algorithm was applied to GNSS/INS(Global Navigation Satellite System/Inertial Navigation System)tight integrated navigation system.The simulation results show that the algorithm can improve the navigation accuracy and robustness,and can still meet the accuracy requirements of navigation in the mutation state.

关 键 词：容积卡尔曼滤波 容积点采样规则 强跟踪滤波 渐消因子 组合导航 

分 类 号：TN713[电子电信—电路与系统] TN967.2