In this paper, a new distributed robust filtering problem is investigated for a class of discrete-time systems subject to parameter uncertainty over sensor networks under the event-based transmission mechanism, where the uncertainty in the target plant is norm-bounded. To save limited network resources, an event-based communication strategy is presented to arrange data scheduling, where the next triggering time of each node can be predicted, and thus continuous listening is avoided. By employing the robust regularized least-squares estimation approach, a local robust recursive algorithm is first developed by minimizing the given quadratic cost index. Then, a fully distributed information fusion scheme is implemented by exchanging information between adjacent nodes, where the local information spreads to the entire network after a finite-step iteration under the connected topology. Furthermore, by utilizing the matrix inequality technique and the mathematical induction approach, some sufficient conditions are derived to ensure the uniform boundedness of the weighting matrices. Finally, an illustrative example is given to validate the developed distributed robust filtering algorithm.

中文翻译：

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传感器网络上基于事件的分布式鲁棒过滤的正则化最小二乘法

本文针对基于事件的传输机制下传感器网络参数不确定性的一类离散时间系统，研究了一种新的分布式鲁棒滤波问题，其中目标对象的不确定性是范数有界的。为了节省有限的网络资源，提出了基于事件的通信策略来安排数据调度，可以预测每个节点的下一次触发时间，从而避免连续监听。通过采用鲁棒正则化最小二乘估计方法，首先通过最小化给定的二次成本指数来开发局部鲁棒递归算法。然后，通过相邻节点之间交换信息来实现全分布式信息融合方案，其中局部信息在连通拓扑下经过有限步迭代后传播到整个网络。此外，利用矩阵不等式技术和数学归纳法，导出了保证加权矩阵一致有界性的充分条件。最后，给出了一个说明性例子来验证所开发的分布式鲁棒过滤算法。