Consensus filtering algorithm based on dual event triggering

In order to reduce the transmission frequency and computational burden of the consensus estimation algorithm for distributed sensor networks, a consensus filtering algorithm based on dual event triggering is proposed. In the algorithm, for data transmission, an adaptive trigger condition is designed by using the information vector error function to remove redundant transmission, for consensus iteration, a static event trigger is designed, the information vector error between each moment and the previous moment is used as the trigger condition to reduce the calculation amount. Furthermore, the boundedness of the improved algorithm is proved by using Lyapunov stability theory and linear matrix inequality method. Simulation results show that the algorithm can effectively reduce redundant data transmission and consensus calculation. Compared with the algorithms in the existing literature, the proposed algorithm has higher estimation accuracy under the same trigger frequency.