基于最大熵Kalman滤波的机载SINS自标定技术

Airborne SINS Self-Calibration Technology Based on Maximum Correntropy Kalman Filter

针对在机载捷联惯导系统(SINS)自标定过程中,量测噪声呈非高斯分布,导致经典Kalman滤波性能降低的问题,该文提出了基于最大熵Kalman滤波(MCKF)的机载SINS自标定技术。该方法采用最大相关熵准则(MCC)替代经典Kalman滤波的最小均方误差准则,有效利用信号的高阶矩信息,并将其应用于机载SINS自标定系统中。仿真结果表明,在非高斯噪声条件下,该方法能够估计出机载SINS待标定参数,且算法的鲁棒性和误差项估计精度均优于经典Kalman滤波,具有一定的工程应用价值。

Aiming at the problem that the measurement noise presents a non-Gaussian distribution in the self-calibration process of the airborne strapdown inertial navigation system(SINS),which leads to the degradation of the performance of the classic Kalman filter,an airborne SINS self-calibration technology based on the maximum entropy Kalman filter(MCKF)is proposed in this paper.In this method,the maximum correntropy criterion(MCC)is used to replace the minimum mean square error criterion of classical Kalman filter,the high-order moment information of the signal is effectively utilized,and it is applied to the airborne SINS self-calibration system.The simulation results show that this method can estimate the parameters to be calibrated on the airborne SINS in the condition of non Gaussian noise,and the robustness and error estimation accuracy of the algorithm are better than those of the classical Kalman filter,which has certain engineering application value.