Improved Nelder-Mead based KF Method for Accurate Localization

Le résumé fourni par la source

An improved Kalman filter (KF) method based on Nelder-Mead (NM) optimization is proposed to address the issues of slow convergence speed of KF, unstable state estimation, and challenging commencing parameter selection for model parameters in non-smooth environments. First of all, by employing the NM technique to determine the optimal covariance matrix initially value based on the smoothness indicators and root-mean-square error (RMSE), the convergence speed and stability of the KF are enhanced. Following that, a smoothing gain approach using historical information suppresses the problem of Kalman acquire fluctuation due to catastrophic noise, improving the stability and smoothness of state estimation. Last but not least, in order to boost the robustness, stability, and filtering influence of the KF model, decrease the filter noise sensitivity, and tackle the matrix inversion challenge, the attenuation factor and regularization term have been included to the updating process. The results of the experiments demonstrate that this paper’s method has excellent estimation accuracy and improves the filtering accuracy of straight and curved walking data by 44.6% and 85.5%, respectively, when compared to the existing filtering methods. When the GPS data is missing, the filtering accuracy of straight and curved walking data is improved by 80.6% and 81.1%, respectively. The technique presented in this paper eliminates the challenge of nonlinear models’ performance degradation when working with linear systems by significantly increasing the traditional KF’s convergence speed, stability, and robustness in complex dynamic environments. It additionally obviously improves the accuracy of navigation data for both linear as well as nonlinear systems.

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