ABSTRACT In this paper, the robust distributed filtering problem is investigated for a class of wireless sensor networks with stochastic parameter and topology uncertainties. The local filters collect information not only from itself but also from its neighbouring sensor nodes according to the network topology. A novel robust distributed filter is proposed which takes stochastic parameter and topology uncertainties into full consideration but does not depend on any specific structure of uncertainties. By proper designing of the filter gain, the trace of estimation error covariance is minimised at each time step, where the new stochastic analysis and augmentation transformation techniques are developed to handle the challenges resulting from multiplicative stochastic uncertainties and sparse topology. The corresponding recursive robust distributed filtering algorithm is also presented for real-time online applications. Finally, a simulation study is carried out to illustrate the effectiveness and applicability of our proposed method.

中文翻译：

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具有参数和拓扑不确定性的无线传感器网络分布式滤波

摘要 本文研究了一类具有随机参数和拓扑不确定性的无线传感器网络的鲁棒分布式滤波问题。本地过滤器不仅从自身收集信息，还根据网络拓扑从其相邻传感器节点收集信息。提出了一种新的鲁棒分布式滤波器，它充分考虑了随机参数和拓扑不确定性，但不依赖于任何特定的不确定性结构。通过适当设计滤波器增益，估计误差协方差的轨迹在每个时间步都被最小化，其中开发了新的随机分析和增强变换技术来处理乘法随机不确定性和稀疏拓扑带来的挑战。相应的递归鲁棒分布式过滤算法也被提出用于实时在线应用。最后，进行了仿真研究，以说明我们提出的方法的有效性和适用性。