Student’s t滤波框架下的信息融合算法

针对多传感器系统的观测噪声为非高斯噪声的问题，通过Student’s t滤波框架和四元数特性相结合，以无迹变换计算Student’s t加权积分函数，设计基于Student’s t分布的无迹四元数滤波算法，作为局部滤波算法. 利用拉格朗日乘子法计算最优融合权重系数，通过线性加权融合的方式，对各局部滤波结果进行融合. 采用基于四元数的目标姿态运动模型进行仿真，利用3个星敏感器同时对同一目标进行观测，通过与已有的鲁棒无迹Student’s t滤波（RSTUF）算法对比，验证所提算法的有效性. 仿真结果表明：所提算法在对目标姿态的估计精度、滤波收敛速度及收敛后的数值稳定性方面均高于RSTUF算法；通过多个观测信息互补，提高了估计精度及容错性.

Information fusion algorithm with Student’s t filtering framework

Considering the problem of non-Gaussian measurement noises in multi-sensor system, an unscented quaternion filtering algorithm was designed based on Student's t distribution as a local filtering algorithm, by combining the Student’s t filtering framework with the characteristics of quaternion and calculating the Student’s t weighted integral function by unscented transformation. The optimal fusion weight coefficients were calculated by Lagrange multiplier method, and the local filtering results were fused by linear weighted fusion method. Quaternion was applied for simulation of the target attitude motion model. Three star sensors were used to observe the same target at the same time. The effectiveness of the proposed algorithm was verified through the comparison with the existing robust unscented Student's t filtering (RSTUF) algorithm. The simulation results show that the proposed distributed information fusion algorithm based on Student’s t filtering framework has higher estimation accuracy, convergence speed and numerical stability than RSTUF algorithm, which improves the estimation accuracy and fault tolerance of the algorithm by the complementarity of multi-observation information.