The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavy-tailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter, the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

研究了非高斯但重尾噪声干扰的多传感器系统的顺序融合估计。基于多元t分布和近似t滤波器，提出了序列融合算法。该算法的性能进行了分析，并与比较Ť基于滤波器的集中式批量融合和基于高斯卡尔曼滤波器的最佳集中式融合。理论分析和详尽的实验分析表明，该算法是有效的。作为基于经典高斯卡尔曼滤波器的最优顺序融合算法的推广，该算法在非高斯噪声动态系统的估计中优于基于高斯卡尔曼滤波器的最优集中式批量融合和最优顺序融合。 。