We study the problem of distributed sequential estimation of common states and measurement noise covariance matrices of hidden Markov models by networks of collaborating nodes. We adopt a realistic assumption that the true covariance matrices are possibly different (heterogeneous) across the network. This setting is frequent in many distributed real-world systems where the sensors (e.g., radars) are deployed in a spatially anisotropic environment, or where the networks may consist of sensors with different measuring principles (e.g., using different wavelengths). Our solution is rooted in the variational Bayesian paradigm. In order to improve the estimation performance, the measurements and the posterior estimates are communicated among adjacent neighbors within one network hop distance using the information diffusion strategy. The resulting adaptive algorithm selects neighbors with compatible information to prevent degradation of estimates.

中文翻译：

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未知异构噪声协方差矩阵下的协同序列状态估计

我们通过协作节点网络研究了公共状态的分布式顺序估计和隐马尔可夫模型的测量噪声协方差矩阵的问题。我们采用一个现实的假设，即真正的协方差矩阵在整个网络中可能不同（异质）。这种设置在许多分布式现实世界系统中很常见，其中传感器（例如，雷达）部署在空间各向异性环境中，或者网络可能由具有不同测量原理（例如，使用不同波长）的传感器组成。我们的解决方案植根于变分贝叶斯范式。为了提高估计性能，测量值和后验估计值在一个网络跳距离内的相邻邻居之间使用信息扩散策略进行通信。