Abstract Distributed adaptive learning algorithms have played a critical role in signal processing and parameter estimation over networks. Most existing algorithms are based on the mean-square error (MSE) criterion, and they can achieve desirable performance when the noise is modeled as Gaussian. However, the performance of MSE-based algorithms may degrade dramatically with the impulsive noise. Therefore, the aim of this paper is to present a diffusion algorithm, named generalized correntropy-based logarithmic difference (d-GCLD) algorithm, for distributed estimation that incorporates robustness to wireless sensor networks (WSNs). By combining the logarithm operation and the correntropy criterion as the loss function, the proposed algorithm is robust to impulsive noise and achieves satisfactory performance in various situations. In addition, the stability problem is studied theoretically. Experimental results are given to demonstrate the validity of the new algorithm in different scenarios.

中文翻译：

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基于广义相关熵对数差分算法的无线传感器网络鲁棒分布式估计

摘要 分布式自适应学习算法在网络信号处理和参数估计中发挥了关键作用。大多数现有算法基于均方误差 (MSE) 准则，当噪声建模为高斯时，它们可以获得理想的性能。然而，基于 MSE 的算法的性能可能会随着脉冲噪声而急剧下降。因此，本文的目的是提出一种扩散算法，称为基于广义相关熵的对数差分 (d-GCLD) 算法，用于分布式估计，它结合了无线传感器网络 (WSN) 的鲁棒性。通过结合对数运算和相关熵准则作为损失函数，该算法对脉冲噪声具有鲁棒性，在各种情况下都取得了令人满意的性能。此外，稳定性问题进行了理论上的研究。实验结果证明了新算法在不同场景下的有效性。