测量噪声方差未知的多传感器组合导航集中融合算法

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%。

Centralized fusion algorithm of multi-sensor integrated navigation for unknown measurement noise variance

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