信号处理2025，Vol.41Issue(7)：1153-1164,12.DOI:10.12466/xhcl.2025.07.002
量测噪声未知Markov跳变系统变分贝叶斯辅助粒子滤波
Variational Bayesian Auxiliary Particle Filter for Jump Markov Systems with Unknown Measurement Noises
摘要
Abstract
The jump Markov system estimation problem involves estimating the state and system mode based on a se-quence of noisy measurements.In some practical applications,changes in sensor conditions and external random inter-ferences can lead to variations in measurement noise.This variability can render the jump Markov system model inaccu-rate,resulting in degraded estimates of the state and system mode.To account for these changing conditions,the mea-surement noise covariance matrix of jump Markov systems is modeled as a discrete stochastic process,with its prior probability distribution assigned as an inverse Wishart distribution.Additionally,dynamic equations for the hyperparam-eters of the measurement noise covariance matrix are defined.A new variational Bayesian auxiliary particle filter is pro-posed to sequentially approximate the joint posterior probability distribution associated with the system mode,state,and measurement noise covariance matrix.The joint posterior distribution of the system mode,state,and noise covari-ance matrix is marginalized with respect to the system mode.The marginalized posterior distribution of the mode is then approximated using an auxiliary particle filter,and the state and noise covariance matrix,conditioned on each particle of the mode variable,are updated using variational Bayesian inference,with conjugacy for the state and noise covari-ance matrix preserved at all times.A simulation study is conducted to compare the proposed method with state-of-the-art approaches in the context of radar target tracking.The simulation results show that the estimation accuracy for the state and noise covariance matrix can be effectively improved,ensuring system mode identification accuracy at the cost of higher computational complexity.关键词
Markov跳变系统/状态估计/辅助粒子滤波/变分贝叶斯推断Key words
jump Markov systems/state estimation/auxiliary particle filter/variational Bayesian inference分类
信息技术与安全科学引用本文复制引用
程承,毛德华,赵斌,孙瑾秋,周军..量测噪声未知Markov跳变系统变分贝叶斯辅助粒子滤波[J].信号处理,2025,41(7):1153-1164,12.基金项目
国家自然科学基金(62373307)The National Natural Science Foundation of China(62373307) （62373307）
