Investigation of the Operation of the Extended Kalman Filter Supplemented by an Adaptive Digital Filter for Integrating Data from a Mobile Robot Control System

Purpose of reseach . The article deals with the study of the operation of the extended Kalman filter (EKF), supplemented with an adaptive digital filter in order to compensate for the error in the operation of the EKF when performing data integration of the mobile robot control system. Methods . The adaptive digital filter (ADF) is a self-tuning filter that iteratively changes its variable parameters to achieve the optimal desired values of the output data. The EKF supplemented with the ADF with the NLMS adaptation algorithm will be called the EKF–ADF system or the EKF+NLMS digital filter. An important task is the selection of the number of frames and ADF weighting coefficients at which the optimum quality of noise suppression and the convergence rate of the adaptation algorithm are achieved. Results . With various options of organizing the operation of the ADF buffer memory, the adjusted values for assessing the state of the ‘mobile robot–environment’ system may differ. When the number of input data frames and ADF weighting coefficients are small, low quality noise suppression will be observed. With an increase in the number of frames and weighting coefficients, the quality of noise suppression is improved, and the convergence rate of the adaptation algorithm decreases. Conclusion . The EKF+NLMS digital filter algorithm takes an intermediate place between the EKF algorithm and the serial filtering of the EKF and ADF signals with the NLMS algorithm according to the criteria for estimating the mean square error, mean absolute error, signal-to-noise ratio, and convergence rate.