Adaptive polynomial Kalman filter for nonlinear state estimation in modified AR time series with fixed coefficients

This article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time-series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root-mean-square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root-mean-square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience. The article presents a novel state estimation method for a modified autoregressive time series, utilizing an adaptive polynomial Kalman filter. This approach dynamically adjusts to changing system dynamics by predicting future states through polynomial extrapolation. image