UKF在惯导平台误差系数辨识离心机测试中的应用

针对惯导平台误差系数辨识的离心机测试,利用直接法建立了误差系数辨识的非线性模型,并结合实际系统模型的特点对标准UKF算法进行了简化和改进.改进后的UKF结构简单,与标准UKF具有同样的滤波精度,并且减小了计算量,提高了计算效率.然后利用扩展Kalman滤波(EKF)算法和改进的UKF算法对惯导平台误差系数辨识离心机测试进行仿真.结果表明,与EKF算法相比,改进的UKF算法能提高惯导平台误差系数的辨识精度,并且更容易实现.

Application of unscented Kalman filter in identification of INS error parameters based on centrifuge test

In order to identify the error parameters of inertial navigation platform in the centrifuge testing, a non-linear model of INS for the identification with a direct method is established. According to the actual system model, the standard unscented Kalman filter(UKF) algorithm is simplified and improved. Compared with the standard UKF, the improved algorithm has the same filtering precision, simpler configuration and lower calculation load. An extended Kalman filter(EKF) algorithm and the improved UKF algorithm are applied respectively to identify the error parameters of INS based on centrifuge test. The simulation results demonstrate that the improved UKF algorithm is more precise in INS error parameter identification and easier to implement than EKF algorithm.