Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

中文翻译：

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用于传感器网络分布式估计的扩散贝叶斯子带自适应滤波器


传感器网络是物联网（IoT）中不可或缺的一部分，传感器执行数据采集和信息处理任务以获得感兴趣的参数，以便基于物联网的监测、诊断等系统对不断变化的工况、瞬时故障做出快速响应等。分布式估计算法通常用于估计这些基于物联网的应用中感兴趣的参数。然而，当传感器网络具有高度相关的输入信号和非平稳行为（其中感兴趣的参数随时间变化）时，传统的分布式估计算法由于输入信号的协方差矩阵中的特征值分布较大而导致学习性能严重下降。感兴趣的参数的随机扰动。为了解决这些问题，本文从贝叶斯学习的角度提出了两种扩散贝叶斯子带自适应滤波器（DBSAF）算法。由于高度相关的输入信号在多频带结构中被白化，并且通过执行贝叶斯推理获得感兴趣参数的不确定性估计，因此与竞争的扩散算法相比，所提出的 DBSAF 算法能够实现更好的学习性能。分析了所提出的 DBSAF 算法的瞬态和稳态均方误差性能，并通过数值模拟进行了验证。推导出时变步长的下限，以在非平稳情况下保持最佳稳态性能。还提出了一种估计噪声方差的新方法。数值模拟表明，与基准算法相比，所提出的算法具有出色的学习性能。