计算机工程与应用2020，Vol.56Issue(1)：257-264,8.DOI:10.3778/j.issn.1002-8331.1809-0206
基于MCC的鲁棒高阶CKF在组合导航中的应用
Application of Robust High-Degree CKF Based on MCC in Integrated Navigation
摘要
Abstract
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.关键词
组合导航/非高斯噪声/鲁棒滤波/MCC估计、Key words
integrated navigation/non-Gaussian noise/robust filter/Maximum Correntropy Criterion(MCC)estimation分类
信息技术与安全科学引用本文复制引用
卢航,郝顺义,彭志颖,黄国荣..基于MCC的鲁棒高阶CKF在组合导航中的应用[J].计算机工程与应用,2020,56(1):257-264,8.基金项目
航空科学基金(No.20110896009,No.20155596024) （No.20110896009,No.20155596024）
陕西省自然科学基金(No.2017JQ6034) （No.2017JQ6034）
民机专项基金(No.MJZ-2014-S-47). （No.MJZ-2014-S-47）
