[an error occurred while processing this directive] [an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]

弹箭与制导学报 >

2025 , Vol. 45 >Issue 6: 1303 - 1309

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.06.042

学术文章

一种具有自适应核带宽的组合导航最大相关熵滤波算法

  • 林雪原 , 1 ,
  • 潘新龙 , 2, * ,
  • 朱振球 3 ,
  • 李书锋 1
展开
  • 1 山东外事职业大学人工智能学院,山东 威海 264500
  • 2 海军航空大学,山东 烟台 264001
  • 3 中国人民解放军第32006部队,北京 100000
潘新龙(1983—),男,副教授,博士。E-mail:。

林雪原(1970—),男,教授,博士。E-mail:。

收稿日期: 2025-05-16

  网络出版日期: 2026-01-24

基金资助

国家自然科学基金(62076249)

山东省自然科学基金(ZR2020MF154)

收起

Integrated Navigation Maximum Correntropy Filtering Algorithm with Adaptive Kernel Bandwidth

  • LIN Xueyuan , 1 ,
  • PAN Xinlong , 2, * ,
  • ZHU Zhenqiu 3 ,
  • LI Shufeng 1
Expand
  • 1 School of Artificial Intelligence,Shandong Vocational University of Foreign Affairs,Weihai 264500,Shandong,China
  • 2 Institute of Information Fusion,Naval Aeronautical University,Yantai 264500,Shandong,China
  • 3 The 32006 Unit of the People’s Liberation Army,Beijing 100000,China

Received date: 2025-05-16

  Online published: 2026-01-24

Fold

摘要

最大相关熵卡尔曼滤波器(MCKF)可以提高复杂环境下组合导航系统的滤波精度,而核带宽的取值对MCKF滤波精度影响较大,为此如何正确选择核宽度是MCKF中的一个关键问题。根据滤波新息及其理论协方差构建自适应因子,并根据自适应因子及选定的最大核带宽进行在线自适应调整核带宽以消除核带宽对MCKF性能的影响,进而提出了一种具有自适应核的MCKF(AMCKF)。以GNSS/SINS组合导航系统对AMCKF进行了实验验证。实验结果表明,AMCKF具有在线自适应调整核带宽的能力、并能快速获得核带宽的最优值;相对于常规卡尔曼滤波器(KF)及具有强鲁棒性的MCKF(核带宽为2.5),AMCKF可提高位置精度分别约55.5%和24.1%,可提高速度精度分别约为34.9%和27.7%。

关键词： 最大相关熵卡尔曼滤波器; 核带宽; 组合导航系统; 自适应性

本文引用格式

林雪原 , 潘新龙 , 朱振球 , 李书锋 . 一种具有自适应核带宽的组合导航最大相关熵滤波算法[J]. 弹箭与制导学报, 2025 , 45(6) : 1303 -1309 . DOI: 10.15892/j.cnki.djzdxb.2025.06.042

Abstract

Maximum correntropy Kalman filter (MCKF) can improve the filtering accuracy of integrated navigation system in complex environment,but MCKF with fixed kernel bandwidth may cause the filtering accuracy to decrease.Therefore,how to correctly select the kernel width is a key problem in MCKF.In this paper,an adaptive factor is constructed according to the filter innovation and its theoretical covariance,and the kernel bandwidth is adjusted online according to the adaptive factor and the selected maximum kernel bandwidth to eliminate the influence of kernel bandwidth on MCKF performance,and then an adaptive kernel MCCKF (AMCCKF) is proposed.The GNSS/SINS integrated navigation system is used to verify the AMCCKF.The experimental results show that AMCCKF has the ability to adjust the kernel bandwidth on line and obtain the optimal value of the kernel bandwidth quickly.Compared with the conventional Kalman Filter (KF) and the highly robust MCKF (with a Kernal bandwidth of 2.5),AMCKF can improve the position accuracy by approximately 55.5% and 24.1% respectively,and the velocity accuracy by approximately 34.9% and 27.7% respectively.

Key words： Maximum Correntropy Kalman filter; kernal bandwidth; integrated navigation; adaptability

[an error occurred while processing this directive]

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
林雪原, 潘新龙, 丁秋娴, 等. 基于衰退记忆因子的GNSS/SINS组合导航系统VBKF算法[J/OL]. 弹箭与制导学报. https://link.cnki.net/urlid/61.1234.tj.20250122.1624.034.

LIN X Y, PAN X L, DING Q X, et al. VBKF Algorithm for GNSS/SINS Integrated Navigation System based on Fading Memory Factor[J/OL]. Journal of Projectiles,Rockets,Missiles and Guidance. https://link.cnki.net /urlid/61.1234.tj.20250122.1624.034.

[2]
孙玉梅, 潘新龙, 王美春. 基于VB-AUKF的GNSS/SINS组合导航信息融合算法[J]. 弹箭与制导学报, 2023, 43(3):1-8.

DOI

SUN Y M, PAN X L, WANG M C. Information Fusion Algorithm of GNSS/SINS Integrated Navigation System based on VB-AUKF[J]. Journal of Projectiles,Rockets,Missiles and Guidance, 2023, 43(3):1-8.

[3]
秦永元, 张洪钺, 王淑华. 卡尔曼滤波与组合导航原理[M]. 西安: 西北工业大学出版社, 2012.

QIN Y Y, ZHANG H Y, WANG S H. Kalman filter and integrated navigation principle[M]. Xi’an: Northwest University of Technology Press, 2012.

[4]
孙玉梅, 王学伟, 任宪洁. 一种GNSS/SINS组合导航的改进鲁棒滤波算法[J]. 弹箭与制导学报, 2024, 44(1):6-12.

DOI

SUN Y M, WANG X W, REN X J. An Improved Robust Filtering Algorithm for GNSS/SINS Integrated Navigation System[J]. Journal of Projectiles,Rockets,Missiles and Guidance, 2024, 44(1):6-12.

[5]
CHEN B D, LIU X, ZHAO H, et al. Maximum correntropy Kalman filter[J]. Automatica, 2017, 76(1):70-77.

DOI

[6]
CHEN B D, WANG J J, ZHAO H Q, et al. Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion[J]. IEEE Signal Processing Letters, 2015, 22(10):1723-1727.

DOI

[7]
DENG Z H, SHI L, YIN L J, et al. UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise[J]. IEEE Sensor Journal, 2020, 20(14):7766-7773.

DOI

[8]
FAN X X, WANG G, HAN J C, et al. Interacting Multiple Model Based on Maximum Correntropy Kalman Filter[J]. IEEE Transactions on Circuits and Systems-II:Express Briefs, 2021, 68(8):3017-3021.

DOI

[9]
LYU X, HU B Q, CHANG L B, et al. An Adaptive and Robust UKF Approach Based on Gaussian Process Regression-Aided Variational Bayesian[J]. IEEE Sensor Journal, 2021, 21(7):9500-9514.

DOI

[10]
WANG G Q, WANG Z X, WANG Y G, et al. Adaptive Maximum Correntropy Gaussian Filter Based on Variational Bayes[J]. Sensors, 2018, 23(18):1960-1973.

DOI

[11]
LI Z K, CHEN W Q, SUN Y W, et al. An Improved Multiple-Outlier Robust Filter Based on Maximum Correntropy Criterion for Integrated Navigation[J]. IEEE Sensor Journal, 2023, 23(15):17451-17461.

DOI

[12]
CINAR G T, PRINCIPE J C. Hidden state estimation using the correntropy filter with fixed point update and adaptive kernel size[C]// International Joint Conference on Neural Networks,2012:1-6.

[13]
HOU B W HEZ, ZHOU X, et al. Maximum correntropy criterion Kalman filter for α-jerk tracking model with nonGaussian noise[J]. Entropy, 2017, 19(12):648.

DOI

[14]
SEYED F, REZA I, AZIN S, et al. Maximum Correntropy Criterion Kalman Filter with Adaptive Kernel Size[C]//IEEE National Aerospace and Electronics Conference (NAECON),2019:581-584.

[15]
林雪原, 潘新龙, 王玮. 基于最大相关熵准则的GNSS/SINS组合导航滤波算法[J]. 大地测量与地球动力学, 2024, 44(8):787-792.

LIN X Y, PAN X L, WANG W. GNSS/SINS integrated navigation filtering algorithm based on maximum entropy criterion[J]. Journal of Geodesy and Geodynamics, 2024, 44(8):787-792.

[16]
IZANLOO R, FAKOORIAN S A, YZDI H S. Kalman Filtering Based On the Maximum Correntropy Criterion in the Presence of Non-Gaussian Noise[C]// In Princeton: Annual Conference on Information Science and Systems,2016:500-505.

[17]
BAI M M, HUANG Y L, CHEN B D, et al. A Novel Robust Kalman Filtering Framework Based on Normal-Skew Mixture Distribution[J]. IEEE Transactions on Systems, Man and Cybernetics:Systems, 2022, 52(11):6789-6805.

[18]
LI X Y, GUO Y D, MENG Q W. Variational Bayesian-Based Improved Maximum Mixture Correntropy Kalman Filter for Non-Gaussian Noise[J]. Entropy, 2022, 24(2):117-134.

DOI

[19]
LIU B H, LIU X C, JIA S Y. Variational Bayesian-Based Adaptive Maximum Correntropy Generalized High-Degree Cubature Kalman Filter[J]. Circuits,Systems,and Signal Processing, 2023, 42(2):7073-7098.

DOI

[20]
林雪原, 孙炜玮. GNSS/SINS组合导航系统的非线性最大相关熵UKF算法[J/OL]. 武汉大学学报(信息科学版),https://doi.org/10.13203/j.whugis20240045.

LIN X Y, SUN W W. Nonlinear Maximum Correntropy UKF Algorithm for GNSS/SINS Integrated Navigation System[J/OL]. Geomatics and Information Science of Wuhan University,https://doi.org/10.13203/j.whugis20240045.

[21]
CHAMBERS J. A robust Gaussian approximate fixed interval smoother for nonlinear systems with heavy-tailed process and measurement noises[J]. IEEE Signal Processing Letters, 2016, 23(4):468-472.

DOI

[22]
SHAO J B, CHEN W, ZHANG Y, et al. Adaptive Multikernel Size-Based Maximum Correntropy Cubature Kalman Filter for the Robust State Estimation[J]. IEEE Sensor Journal, 2022, 22(20):19835-19844.

DOI

Options
文章导航

/

〈 〉

[an error occurred while processing this directive]