Limited Memory Measurement Noise Adaptive Random Weighted Filtering Algorithm

A new adaptive random weighted filtering algorithm is proposed. It is based on online estimation of limited memory measurement noise to overcome the problem of low filtering precision caused by arithmetic average estimation of measurement noise and its covariance matrix in the existing Kalman filtering algorithm of limited memory online estimation of measurement noise. This method establishes the stochastic weighting theory to estimate the measurement noise online and its covariance by adaptive adj usting the weights of measurement noise statistics. The weight of measurement noise statistics is used to suppress the influence of measurement noise on state estimation and improve the accuracy of filter estimation. Through simulations and analysis, the superiority of the proposed adaptive random weighted filtering algorithm based on online estimation of limited memory measurement noise algorithm is proved.