Adaptive Event-triggered Robust Filtering over Sensor Networks with Markov Switching Topologies

This paper studies the problem of robust state estimation for a class of filter networks based on adaptive event-triggered mechanism. Firstly, the adaptive trigger mechanism is introduced to reduce the communication burden of the filter network. Then, Markov chain is used to describe the random switching of filter network communication. The purpose of this paper is to obtain a distributed full-order filter under the condition of linear matrix inequalities (LMIs), which ensure the filtering dynamic system exponentially mean square stable under the given l
₂
-l
_∞
performance index. Finally, the effectiveness and flexibility of the design method are verified by a simulation example.