期刊名称：EURASIP Journal on Advances in Signal Processing
印刷版ISSN：1687-6172
电子版ISSN：1687-6180
出版年度：2020
卷号：2020
期号：1
页码：1
DOI：10.1186/s13634-020-00670-x
出版社：Hindawi Publishing Corporation
摘要：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.
关键词：Target tracking ; Variational Bayesian ; Model set adaptive ; System model uncertainty ; Rényi information divergence
