Sequential fusion estimation for multisensor systems with non-Gaussian noises

Liping Yan, Chenying Di, Q. M. Jonathan Wu, Yuanqing Xia

Abstract: 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.