Asynchronous multi-rate systems of a transfer learning filtering algorithm using t-distribution
WANG Wei
ZHAO Shun-yi
ZHANG Cheng-xi
LUAN Xiao-li
LIU Fei
WU Jin
Abstract：This paper investigates a transfer learning filtering algorithm based on the t-distribution to address the prob-lem of state estimation in asynchronous multi-rate sensor systems affected by outliers.By integrating full probability design and multi-scale system theory,we propose a novel transfer learning framework for asynchronous multi-rate systems.A multi-scale model is established to convert the asynchronous multi-rate system into a synchronous one.This design min-imizes the Kullback-Leibler divergence between the predicted distribution in the source domain and the ideal distribution in the target domain,while allowing the sensor sampling rate ratio to be any positive integer.To account for outlier effects on state estimation,the source and target domains leverage the heavy-tailed properties of the t-distribution to model state and observation processes,with approximate estimation achieved through expectation-maximization and variational Bayesian methods.Simulation results on a planar position-velocity system demonstrate the superior performance of the proposed method.
Keywords：state estimationasynchronouus multi-rate sensorst-distributiontransfer learningvariational Bayesian
Publication Date：2025-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 947-954 )