基于MCC的鲁棒高阶CKF在组合导航中的应用

针对高阶容积卡尔曼滤波器在非高斯噪声情况下滤波精度下降的问题，提出了一种新的基于Maximum Correntropy Criterion（MCC）的鲁棒高阶容积卡尔曼滤波算法。考虑到高阶容积规则可以较好地解决非线性问题，在高阶容积滤波的基础上，结合统计线性回归模型对量测更新过程进行重构，利用MCC估计算法实现状态的量测更新，同时解决了系统的非线性和非高斯问题。将所提算法应用到SINS/GPS组合导航系统中，仿真结果表明，核宽的选取对算法的滤波性能有较大的影响，在高斯混合噪声条件下，所提算法相比传统高阶容积卡尔曼滤波算法具有更强的鲁棒性和更高的滤波精度。

Application of Robust High-Degree CKF Based on MCC in Integrated Navigation

A new robust high-degree cubature Kalman filtering algorithm based on Maximum Correntropy Criterion （MCC） is proposed to solve the problem that the filtering precision of High-degree Cubature Kalman Filter（HCKF） decreases when the noise is non-Gaussian. Considering that high degree cubature rule can solve nonlinear problems well, after reconstructing measurement update process by using the statistical linear regression model, the measurement update is implemented by the MCC estimation, the proposed robust high-degree cubature Kalman filter algorithm based on HCKF can solve the problem of nonlinear system and non-Gaussian noise effectively. The proposed algorithm is applied to the SINS/GPS integrated navigation system, the simulation results show that the selection of kernel width has great influence on the filtering performance of the algorithm, and the proposed algorithm has stronger robustness and higher filtering precision than the traditional high-degree cubature Kalman filtering algorithm under the condition of Gaussian mixture noise.