基于预测滤波的捷联惯导任意双位置对准方法  

Method of any two-position alignment based on predictive filter for SINS

作　　者：宫晓琳[1] 房建成[1] 

机构地区：[1]北京航空航天大学仪器科学与光电工程学院,北京100083

出　　处：《北京航空航天大学学报》2008年第5期533-536,563,共5页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家863基金资助项目(2006AA12A108);国家自然科学基金资助项目(60602047)

摘　　要：针对捷联惯性导航系统(SINS,Strapdown Inertial Navigation System)在大失准角情况下的初始对准问题,建立基于加性四元数误差模型的非线性滤波方程,并提出一种基于模型预测滤波(MPF,Model Predictive Filter)与扩展卡尔曼滤波(EKF,Extended Kalman Fil-ter)相结合的地面任意双位置初始对准方法.该方法将部分惯性器件误差作为模型误差,在线实时估计并修正系统模型,提高了状态估计的精度,并克服了将模型误差假设为高斯白噪声的局限性.半物理仿真结果表明,该方法有效提高了SINS姿态误差角的估计精度,而且也降低了系统状态变量的维数,提高了对准解算的实时性.Aiming at the initial alignment of strapdown inertial navigation system （SINS） with a large azimuth misalignment angle, the nonlinear mathematics model based on additive quaternion error（AQE） equations was established. And any two-position alignment method based on model predictive filter（MPF） and extended Kalman filter（EKF） was proposed. In this method, parts of inertial element errors were regarded as the model error, and estimated as part of the solution. The model error was not limited to Gaussian noise characteristics, and the algorithm could be implemented on-line to both filter noisy measurements and estimate state trajectories. The results of tests show that this method has better precision and faster convergence of all misalignment angles especially the azimuth one than that of EKF. Furthermore, this method improves the alignment＇s real time by reducing the number of states.

关 键 词：惯性导航系统 对准 预测 非线性滤波 

分 类 号：V249.322[航空宇航科学与技术—飞行器设计]