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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。
Aiming at the centrifuge test of the error coefficient identification of inertial navigation platform, a nonlinear model of error coefficient identification is established by using the direct method. The standard UKF algorithm is simplified and improved according to the characteristics of the actual system model. The improved UKF has a simple structure, has the same filtering accuracy as the standard UKF, and reduces the computational complexity and improves the computational efficiency. Then, the Extended Kalman Filter (EKF) algorithm and the improved UKF algorithm were used to simulate the centrifuge test of the error coefficient of INS. The results show that, compared with EKF algorithm, the improved UKF algorithm can improve the identification accuracy of error coefficient of INS and can be realized more easily.