Abstract In this paper, we investigate the distributed state estimation of non-Gaussian systems, where every sensor only exchanges information within its neighborhoods in the absence of a fusion center. Taking advantage of the Gaussian correntropy in processing non-Gaussian signals, we first derive a centralised maximum correntropy Kalman filter for linear multi-sensor systems, and then obtain its information form with some approximations. After that, a distributed maximum correntropy information filter is designed to approximate the centralised information filter using the consensus average method, and its extension to nonlinear systems is also provided based on statistical linearization. Simulation results on the typical target tracking example over a sensor network are given to illustrate the effectiveness of the proposed algorithms.

中文翻译：

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非高斯噪声系统的分布式最大相关熵线性和非线性滤波器

摘要在本文中，我们研究了非高斯系统的分布式状态估计，其中每个传感器仅在没有融合中心的情况下在其邻域内交换信息。利用高斯相关熵在处理非高斯信号时的优势，我们首先推导出一个用于线性多传感器系统的集中式最大相关熵卡尔曼滤波器，然后通过一些近似得到它的信息形式。之后，设计了分布式最大相关熵信息过滤器，以使用共识平均方法近似集中式信息过滤器，并基于统计线性化提供了其对非线性系统的扩展。给出了传感器网络上典型目标跟踪示例的仿真结果，以说明所提出算法的有效性。