Distributed wavelet-learning-based fusion estimation for multirate multisensor systems with integer and non-integer multiple sample ratios

This paper explores the asynchronous fusion estimation problem of multirate multisensor systems under sensor time-varying sampling. First, the sensors are divided into integer and non-integer multiples based on the ratio of the sensor sampling rate to the state updating rate. Under the case of integer multiple, a synchronous state space model is established by state projection through wavelet transform, and then a Kalman-based multirate estimator is designed to calculate local estimation. Under the case of no-integer multiple, a measurement compensation mechanism is designed using the measurement information closest to the state updating instant, and then a back propagation neural network is constructed to obtain the corresponding estimation. Based on the above obtained local estimation, a learning-based fusion criterion is developed by taking into account the time difference between the state updating moment and the measurement sampling moment. Finally, the simulation results demonstrate the advantages and effectiveness of the proposed method.