Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence

• Main Authors: Tianli Ma, ChaoBo Chen, Song Gao
• Format: Article
• Language: English
• Published: SpringerOpen 2020-04-01
• Series: EURASIP Journal on Advances in Signal Processing
• Subjects: Target tracking; Variational Bayesian; Model set adaptive; System model uncertainty; Rényi information divergence
• Online Access: http://link.springer.com/article/10.1186/s13634-020-00670-x

Abstract The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information divergence, as a criterion, is to choose the best match model that has the minimum divergence between candidate models and true mode. Subsequently, the model-conditioned estimation based on variational Bayesian approximation is proposed to estimate system state and measurement noise variances. To deal with the coupled noise intractability, the moments matching technique is used to obtain the mixed statistics of measurement noise at the fusion stage. The proposed algorithm is compared with the interacting multiple models (IMM) algorithm and the variational Bayesian-interacting multiple models (IMM-VB) algorithm via two scenarios for maneuvering target tracking, and simulation results show that the MSA-VB has improved estimation and tracking performance.