Research On Distributed Fusion Estimation Algorithm Based On Event Triggerin

In wireless sensor networks,sensors are mainly used to transmit information,but when the bandwidth resource is limited and the carrying capacity of the network is threatened,the performance of the system is reduced and unstable.In order to reduce the energy consumption of communication,reasonable event-triggered mechanisms are studied.At the same time,in order to solve the problem of low accuracy and fault tolerance of the estimation system,some event-triggered multi-sensor distributed fusion estimation schemes are designed.The specific research contents are as follows:Firstly,for linear systems,a reasonable event-triggered mechanism is constructed.according to the filter and forecast of the system,an event-triggered statistic that follows the standard Gaussian distribution is constructed,and the significance of the trigger threshold is given by the hypothesis test based on the Gaussian distribution,an event-triggered Kalman filter is proposed,the theoretical trigger probability and estimation accuracy under the proposed trigger mechanism are derived.Whether it is a steady-state system or a time-varying system,the threshold in the improved algorithm can be reasonably set in advance according to the required accuracy.Subsequently,combined with the improved covariance intersection(CI)fusion estimation algorithm,it not only effectively reduces the communication cost,but also improves the estimation accuracy.Numerical simulations verify the correctness and effectiveness of the proposed theorems and algorithms.Secondly,the fusion structure of the consensus estimation algorithm is flexible and the estimation accuracy is high.In order to reduce the transmission frequency and computational burden of the consensus estimation algorithm in distributed sensor networks,an information weighted consensus filter algorithm based on dual event triggering is proposed.In the algorithm,an adaptive trigger condition is designed for the link of data transmission,and redundant transmission is removed by using the information vector error function.For the link of 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 amount of calculation.In addition,the boundedness of the improved algorithm is proved by using Lyapunov stability theory and linear matrix inequality method.The results of simulation show that the algorithm can effectively reduce redundant data transmission and consensus calculation,and has excellent estimation performance.Finally,for discrete nonlinear systems,a distributed cubature information Kalman filter algorithm based on innovation event trigger and consensus mechanism is designed.When the given threshold is exceeded by the value of the innovation trigger function,the consistent transmission is performed.Otherwise,the predicted value is used instead of the transmission value.After combined the innovation event trigger mechanism,the improved consensus distributed estimation algorithm not only effectively reduces the calculation amount of the traditional distributed cubature information Kalman filter algorithm,but also can guarantee the estimation performance.The simulation verifies the effectiveness of the algorithm.