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弹箭与制导学报 >

2025 , Vol. 45 >Issue 6: 1051 - 1058

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2025.06.012

学术文章

基于衰退记忆因子的GNSS/SINS组合导航系统VBKF算法

  • 林雪原 , 1 ,
  • 潘新龙 , 2, * ,
  • 丁秋娴 1 ,
  • 孙玉霞 1
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  • 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
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: 2024-09-29

  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

VBKF Algorithm for GNSS/SINS Integrated Navigation System based on Fading Memory Factor

  • LIN Xueyuan , 1 ,
  • PAN Xinlong , 2, * ,
  • DING Qiuxian 1 ,
  • SUN Yuxia 1
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  • 1 School of Artificial Intelligence,Shandong Vocational University of Foreign Affairs, Weihai 264500,Shandong,China
  • 2 Naval Aeronautical University,Yantai,264001,China

Received date: 2024-09-29

  Online published: 2026-01-24

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摘要

变分贝叶斯自适应滤波(Variational Bayesian adaptive kalman filter,VBKF)可以有效解决GNSS/SINS组合导航系统因测量噪声异常而导致的滤波精度下降问题。针对VBKF对测量噪声的估计性能严重依赖遗忘因子的问题,基于卡方分布的单侧上位点与单侧下位点,本文提出了测量噪声变化起始时刻与终止时刻检测算法,并构建了衰退记忆因子模型,进而提出了基于衰退记忆因子的VBKF算法,解决了VBKF估计突变噪声的“拖尾”问题。理论仿真实验表明,该算法可准确检测测量噪声突变的起始时刻与终止时刻;当测量噪声方差突变结束的200s时间内,相对于VBKF算法,FMVBKF可提高位置精度约7.5%、提高速度精度约14.7%,而其他时间段内VBKF算法和FMVBKF算法的滤波精度几乎相同。

关键词： 变分贝叶斯; 衰退记忆因子; 自适应滤波; 组合导航系统

本文引用格式

林雪原 , 潘新龙 , 丁秋娴 , 孙玉霞 . 基于衰退记忆因子的GNSS/SINS组合导航系统VBKF算法[J]. 弹箭与制导学报, 2025 , 45(6) : 1051 -1058 . DOI: 10.15892/j.cnki.djzdxb.2025.06.012

Abstract

Variational Bayesian adaptive Kalman filter (VBKF) can effectively solve the problem of filtering accuracy degradation caused by abnormal measurement noise in GNSS/SINS integrated navigation system.Aiming at the problem that VBKF’s estimation performance of measurement noise relies heavily on forgetting factors,based on the unilateral upper and unilateral lower sites of Chi-square distribution,this paper proposes the detection algorithm of the start time and end time of measurement noise change,and constructs the fading memory factor model,and then proposes the VBKF algorithm based on fading memory factor.The “trailing” problem of VBKF estimation of abrupt noise is solved.Theoretical simulation results show that the proposed algorithm can accurately detect the start time and end time of measurement noise mutation.Compared with the VBKF algorithm,FMVBKF can improve the position accuracy by about 7.5% and the velocity accuracy by about 14.7% in 200s when the noise variance change ends,while the filtering accuracy of VBKF algorithm and FMVBKF algorithm is almost the same in other time periods.

Key words： variational bayesian; fading memory factor; Adaptive Filtering; integrated navigation system

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