| 荆 蕾,孙炜玮,潘新龙,乔玉新,韩真真.测量噪声方差未知的多传感器组合导航集中融合算法[J].电子测量与仪器学报,2023,37(10):164-171 |
| 测量噪声方差未知的多传感器组合导航集中融合算法 |
| Centralized fusion algorithm of multi-sensor integrated navigationfor unknown measurement noise variance |
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| DOI： |
| 中文关键词: 变分贝叶斯 自适应卡尔曼滤波 测量噪声方差未知 多传感器组合导航系统 集中式融合算法 |
| 英文关键词:variational Bayesian adaptive Kalman filter unknown measurement noise variance multi-sensor integrated navigation centralized fusion algorithm |
| 基金项目:国家自然科学基金(62076249)、山东省自然科学基金(ZR2020MF154)项目资助 |
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| 中文摘要: |
| 目前,多传感器组合导航系统的信息融合方法是建立在测量噪声方差已知的基础上,然而测量噪声方差会随着内部及
外部的干扰而发生变化。 为此,本文首先将基于变分贝叶斯逼近的自适应卡尔曼滤波( variational Bayesian approximation based
adaptive Kalman filter,VB-AKF)从单一组合导航系统扩展到多传感器组合导航系统;然后,提出了多传感器组合导航系统的两
种集中融合算法,即基于 VB-AKF 的增广式集中融合算法及基于 VB-AKF 的序贯式集中融合算法,以解决测量噪声方差未知情
况下的多传感器组合导航的信息融合问题;最后,通过 SINS / GNSS / CNS / ADS 多传感器组合导航系统对上述算法进行了仿真验
证。 实验结果表明,本文所提两种算法滤波精度相同、且接近于测量噪声方差已知情况下的理想集中融合算法( ICKF)。 在整
个仿真时段内,相对于传统集中式卡尔曼滤波器(TCKF)及具有容错功能的联邦卡尔曼滤波算法(FT-FKF),本文算法可提高位
置精度分别为 32%和 90%、提高速度精度分别为 38%和 71%。 |
| 英文摘要: |
| At present, the information fusion method of multi-sensor integrated navigation system is based on the known variance of
measurement noise, but the variance of measurement noise will change with internal and external interference. Therefore, this paper
firstly extends the variational Bayesian approximation based adaptive Kalman filter (VB-AKF) from a single integrated navigation system
to a multi-sensor integrated navigation system. Then, two kinds of centralized fusion algorithms of multi-sensor integrated navigation
system are proposed, namely, the VB-AKF based augmented centralized fusion algorithm and the VB-AKF based sequential centralized
fusion algorithm, to solve the problem of information fusion of multi-sensor integrated navigation with unknown measurement noise
variance. Finally, the SINS / GNSS / CNS / ADS multi-sensor integrated navigation system is used to validate the above algorithm. The
experimental results show that the two algorithms proposed in this paper have the same filtering accuracy and are close to the ideal
centralized Kalman fusion algorithm ( ICKF) when the variance of measurement noise is known. In the whole simulation period,
compared with traditional centralized Karl filter (TCKF) and federal Kalman filter (FT-FKF) with fault tolerance function, the proposed
algorithm can improve position accuracy by 32% and 90%, and speed accuracy by 38% and 71%, respectively |
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