Sequential fusion for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements

Guiting Hu; Zhengjiang Zhang; Luping Xu

doi:10.1016/j.isatra.2024.11.005

Sequential fusion for multi-rate multi-sensor nonlinear dynamic systems with heavy-tailed noise and missing measurements

ISA Trans. 2024 Nov 10:S0019-0578(24)00516-0. doi: 10.1016/j.isatra.2024.11.005. Online ahead of print.

Authors

Guiting Hu¹, Zhengjiang Zhang², Luping Xu³

Affiliations

¹ School of Aerospace Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
² The National and Local Joint Engineering Laboratory of Electrical Digital Design Technology, Wenzhou University, Zhejiang, Wenzhou 325000, China. Electronic address: zjzhang@wzu.edu.cn.
³ School of Aerospace Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China. Electronic address: Lpxu@mail.xidian.edu.cn.

PMID: 39572341
DOI: 10.1016/j.isatra.2024.11.005

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.

Keywords: Convergence analysis; Heavy-tailed noise; Kalman filter; Missing measurements; Sequential fusion.