基于平滑变结构卡尔曼滤波的机载组合导航算法  

Airborne Integrated Navigation Algorithm Based on Smooth Variable Structure Kalman Filter

作　　者：高春雷[1] 赵宾[1,2] Gao Chunlei;Zhao Bin(Jin Cheng College,Nanjing University of Aeronautics and Astronautics,Nanjing 211156,China;College of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学金城学院,南京211156 [2]南京航空航天大学自动化学院,南京211106

出　　处：《航天控制》2020年第6期9-13,18,共6页Aerospace Control

基　　金：江苏省高等学校自然科学研究项目资助(19KJD590001,18KJB590003);南航金城学院校级科研项目(2017-y-07)。

摘　　要：研究了平滑变结构卡尔曼滤波算法,该算法基于变结构的思想,在卡尔曼滤波中引入变结构的增益,使得估计状态始终沿着真实状态轨迹切换,优化组合导航性能。选取机载捷联惯导系统的误差方程作为组合导航系统的状态方程,以惯导/卫星位置、速度的差值作为量测信息,采用平滑变结构卡尔曼滤波进行SINS/GNSS组合导航算法设计。仿真验证结果表明,该滤波算法克服了常规卡尔曼滤波算法对系统模型和噪声特性的限制,在估计精度、鲁棒性以及稳定性方面均优于常规卡尔曼滤波,对工程应用具有重要的参考价值。The algorithm of smooth variable structure Kalman filter is studied in this paper.The algorithm is based on the idea of variable structure and the variable structure gain is introduced in the Kalman filter,which forces the estimated state always switch along the real state trajectory and optimizes the integrated navigation performance.The error equation of strapdown inertial navigation system is taken as the state equation of the integrated navigation system,and the difference of position/velocity between SINS and GNSS is used as measurement.The smooth variable structure Kalman filter is used to design the SINS/GNSS integrated navigation algorithm.The simulation results show that the limitations on the system model and noise characteristics of conventional Kalman filter algorithm are overcome by applying the proposed algorithm.The estimation accuracy,robustness and stability of integrated navigation can be improved by using the smooth variable structure Kalman filter and important reference value can be served for engineering application.

关 键 词：机载组合导航 平滑变结构 卡尔曼滤波 

分 类 号：V249.3[航空宇航科学与技术—飞行器设计]