Variational Bayesian Importance-Sampling Particle Filter

Xinyu Zhang

doi:10.1016/j.dsp.2026.106531

Variational Bayesian Importance-Sampling Particle Filter

2026-10-02

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DOI：10.1016/j.dsp.2026.106531

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Abstract

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• PF is briefly reviewed, failures under unknown noise and heavy tails are analyzed. • A new importance density is approximated by IW and Student’s t adaptively with VB. • Calculation of predicted state and observation error covariance matrices is designed. • The method is applied to nonlinear models and results are satisfactory.

Keywords:

State estimation

particle filter (PF)

variational Bayesian (VB)

construction of importance density

calculation of error covariance

Variational Bayesian Importance-Sampling Particle Filter

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