Abstract In this paper, we investigate noise covariance adaptive distributed Bayesian filter based on variational Bayesian inference method. In Bayesian filter framework, the joint distribution of state and noise covariance is approximated by variational distributions, where the unknown noise covariance is modeled by inverse-Wishart distribution. With communicating with neighbors, we show that the joint posterior distribution of state and noises can be approximated by recursively performing variational Bayesian expectation (VB-E) and variational Bayesian maximization (VB-M) steps. Then we use the cubature Kalman filter (CKF) to approximate Gaussian interval, and propose a variational Bayesian based distributed adaptive cubature information filter (VB-DACIF). Finally, we illustrate the effectiveness of the proposed estimation algorithm by a cooperative object tracking problem.

中文翻译：

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基于变分推理的分布式噪声自适应贝叶斯滤波器

摘要 在本文中，我们研究了基于变分贝叶斯推理方法的噪声协方差自适应分布式贝叶斯滤波器。在贝叶斯滤波器框架中，状态和噪声协方差的联合分布由变分分布近似，其中未知噪声协方差由逆Wishart分布建模。通过与邻居的通信，我们表明可以通过递归执行变分贝叶斯期望 (VB-E) 和变分贝叶斯最大化 (VB-M) 步骤来近似状态和噪声的联合后验分布。然后我们使用体积卡尔曼滤波器（CKF）来近似高斯区间，并提出一种基于变分贝叶斯的分布式自适应体积信息滤波器（VB-DACIF）。最后，