测试科学与仪器2026，Vol.17Issue(3)：384-394,11.DOI:10.62756/jmsi.1674-8042.2026033
基于混合熵优化下的卡尔曼滤波组合导航算法
Optimized Kalman filtering approach for integrated navigation based on entropy fusion
摘要
Abstract
To address the performance degradation of traditional Kalman filters in time-varying thick-tailed non-Gaussian noise environments,we propose a maximum correntropy Kalman filter(MCKF)algorithm based on distribution entropy(DE),called DE-MCKF.Among them,the DE is used to determine the confusion degree of measurement data errors,combined with the adaptive change strategy to optimize the MCKF under varied non-Gaussian noise while maintaining the filtering effect of the MCKF.Distributing the entropy can timely improve trust in the measurement value when a high entropy value causes over-change,avoiding over-reliance on model predictions and reducing the filtering errors from MCKF misjudgments on the noise.The results show that DE-MCKF reduces the root mean squared error(RMSE)of averaged latitude and longitude error by 62.76%,58.46%,55.11%,and 43.17%compared to KF,DEKF,MCKF and VBKF,respectively.It can effectively improve the positioning accuracy of the system under non-Gaussian noise.关键词
最大熵/分布熵/卡尔曼滤波/非高斯噪声/高斯核函数Key words
maximum correntropy criterion(MCC)/distribution entropy(DE)/Kalman filter/non-Gaussian noise/Gaussian kernel function引用本文复制引用
张翔,周鑫,司涌波,徐万镛,陈光武..基于混合熵优化下的卡尔曼滤波组合导航算法[J].测试科学与仪器,2026,17(3):384-394,11.基金项目
This study was supported in part by National Natural Science Foundation of China(No.52472344),Gansu Provincial Department of Education(No.2023CYZC-32),Gansu Institute of Integrated Circuit Industry(No.[2023]36),and Lanzhou Science and Technology Program(No.2025-GN-1). （No.52472344）
