Optimized Distributed Filtering Over Binary Sensor Network: A Dynamic Event-Triggering Protocol With Token Bucket Specifications

This article deals with the optimized distributed filtering problem with binary measurements for a class of discrete linear time-varying systems. The system and the original measurements are subject to random noise with known statistical information. Two cases of extracting useful measurement information are designed based on binary measurements between two adjacent moments. Furthermore, a novel time-varying threshold strategy is introduced to reduce the impact of the uncertainties from the binary measurements. The dynamic event-triggering protocols under token bucket specifications are employed to schedule the information transmission among neighboring nodes with constrained resources. The former determines the necessity of information transmission, and the latter describes whether the communication resources are sufficient or not. Information is successfully transmitted only when these two conditions (formulated by two indicator variables) are satisfied. A set of locally sufficient conditions is constructed for each node to guarantee the existence of the distributed filter such that the filtering error system satisfies the exponential boundedness in the mean square. The filter parameters are recursively calculated by solving the distributed optimization problems, which are constrained by linear matrix inequalities for each node. Such a structure achieves the desirable scalability of distributed filtering. A simulation example demonstrates the effectiveness of the distributed filtering scheme developed in this article.