This article investigates the event-triggered Kalman consensus filtering (ET-KCF) problem for distributed sensor networks with intermittent observations. First, a novel ET consensus filtering structure is designed for sensor networks with intermittent observations. With the proposed consensus filtering structure, a new ET mechanism that is more efficient than the existed ones is designed to schedule transmissions of local estimates. Then, an optimal ET-KCF in the sense of minimum mean-square error is developed. For reducing the computational complexity of filtering algorithm, a suboptimal ET-KCF is further proposed. Moreover, the stability of the suboptimal ET-KCF is analyzed. Simulation results verify the validity and superiority of the proposed method.

中文翻译：

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用于具有间歇性观察的传感器网络的事件触发卡尔曼一致性滤波器

本文研究了具有间歇性观察的分布式传感器网络的事件触发卡尔曼共识过滤 (ET-KCF) 问题。首先，为具有间歇性观察的传感器网络设计了一种新颖的 ET 共识过滤结构。通过提议的共识过滤结构，设计了一种比现有机制更有效的新 ET 机制来安排本地估计的传输。然后，开发了最小均方误差意义上的最佳 ET-KCF。为了降低滤波算法的计算复杂度，进一步提出了次优ET-KCF。此外，还分析了次优 ET-KCF 的稳定性。仿真结果验证了所提方法的有效性和优越性。