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.

针对具有模型和噪声不确定性的目标跟踪系统，提出了一种基于变分贝叶斯近似（MSA-VB）的模型集自适应滤波算法。作为标准，Rényi信息差异是选择候选模型与真实模式之间具有最小差异的最佳匹配模型。随后，提出了基于变分贝叶斯近似的模型条件估计，以估计系统状态和测量噪声方差。为了处理耦合噪声的难处理性，使用矩匹配技术来获得融合阶段测量噪声的混合统计量。