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Sequential fusion for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing
Guiting Hu1, Zhengjiang Zhang2, Luping Xu1
1School of Aerospace Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
Abstract:
This paper focuses on sequential fusion estimation for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements. On the basis of Bayesian inference, a sequential Student's t-based unscented Kalman filter (SSTUKF), together with its square-root form (SR-SSTUKF), is proposed by using the unscented transform to calculate Student's t weighted integrals. Considering the nonstationary measurement noise and/or accumulated computation error, adaptive factors are introduced by the t-test to suppress uncertainties. Additionally, the complexity computation and convergence analysis of the SR-SSTUKF are presented. The validity and robustness of the proposed sequential fusion method are illustrated by an example of agile target tracking. Simulation results indicate that the SR-SSTUKF with adaptive factors can further enhance accuracy and yield reliable estimations.
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