A new adaptive Kalman filter by combining evolutionary algorithm and fuzzy inference system

The performance of the Kalman filter (KF), which is recognized as an outstanding tool for dynamic system state estimation, heavily depends on its parameter R, called the measurement noise covariance matrix. However, it´s difficult to get the exact value of R before the filter starts, and the value of R is likely to change with the measurement environment when the filter is working. To solve this problem, a new parameter adaptive Kalman filter is proposed in this paper. In this new Kalman filter, the initial value of R is offline decided by Evolutionary Algorithm (EA), and the value of R decided by EA is online updated by Fuzzy Inference System (FIS). A simulation experiment based on target tracking is carried out, and the results demonstrate that the new adaptive Kalman filter proposed in this paper (HydGeFuzKF) has a stronger adaptability to time-varying measurement noises than regular Kalman filter (RegularKF), Sage-Husa adaptive Kalman filter (SageHusaKF), the adaptive Kalman filter only based on genetic algorithm (GeneticKF) and the adaptive Kalman filter only based on fuzzy inference system (FuzzyKF).