基于MEP-UKF的组合导航滤波算法  

Integrated Navigation Filtering Algorithm Based on MEP-UKF

作　　者：王小旭 赵琳[1] 

机构地区：[1]哈尔滨工程大学自动化学院,哈尔滨150001

出　　处：《宇航学报》2010年第2期432-439,共8页Journal of Astronautics

基　　金：国家自然科学(60474046)

摘　　要：针对目前应用于SINS/GPS组合导航系统中的扩展卡尔曼滤波(Extended Kalman Filter,EKF)存在精度低、实时性差的缺点,提出一种基于模型误差预测(Model Error Prediction,MEP)的Unscented卡尔曼滤波(Unscented Kalman Filter,UKF)。MEP-UKF滤波算法将惯性器件测量误差作为模型误差使用MEP进行实时预测的同时,采用UKF估计载体的姿态、速度及位置等误差信息,并反馈给SINS系统来校正导航参数。MEP-UKF不仅克服了UKF必须假设惯性器件误差为高斯白噪声的局限性,而且降低了SINS/GPS组合导航系统状态变量的维数,大大缩短了导航解算的时间。仿真结果表明,MEP-UKF的收敛速度和滤波精度均明显优于EKF,更好地满足了工程应用中对导航精度和实时性的要求。A method of combining model error prediction （MEP） and unscented Kahnan filter （UKF） was put forward to solve the problems of low accuracy and poor real time, which consist in extended Kalman filter （EKF） applied in SINS/GPS integrated navigation system. MEP-UKF filtering algorithm considers the measurement error resulted from the inertial unit as the model error and predicts it in time through MEP. Then it uses UKF to estimate the vehicle error information, including attitude, velocity and position, which are fed back to SINS to correct the navigation parameters. Consequently, MEP-UKF not only avoids the limitation that UKF has to assume the measurement error as the Gaussian white noise, but also decreases the state dimension of SINS/GPS integrated navigation system, further shorten the navigation calculation time. Simulation results show that MEP-UKF is superior to EKF in convergence and precision, and satisfies the requirements of navigation precision and real time which is emphasized in project.

关 键 词：SINS/GPS组合导航系统 模型误差预测 UNSCENTED卡尔曼滤波 导航精度 实时性 

分 类 号：V249.328[航空宇航科学与技术—飞行器设计] TP202.2[自动化与计算机技术—检测技术与自动化装置]