Event-Triggered Fast Information Consensus Filter

In distributed state estimation for linear systems with multi-sensor networks, the event-triggered fast information consensus filtering algorithm (ET-FICF) is proposed to address the problems of high communication burden and high number of iterations of the information consensus algorithm. The algorithm utilises a distributed fast consensus weighting algorithm based on alternating direction multiplier method (ADMM) to obtain the weight matrix. For the consensus iteration step, a specific trigger mechanism is designed based on hypothesis testing of Gaussian distribution. The proposed ET-FICF algorithm not only effectively reduces the communication burden, but also has a high consensus speed. Finally, the correctness and effectiveness of the proposed algorithm are verified by simulation examples.