An adaptive algorithm based on levenberg-marquardt method and two-factor for iterative extended Kalman filter

Abstract

The iterative extended Kalman filter (IEKF) algorithm is dependent on the previous data and estimation accuracy of the noise. The noise will result in filtering precision decrease and divergence. This paper puts forward an adaptive algorithm based on Levenberg-Marquardt method and two-factor for IEKF, which optimizes the iterative process by using Levenberg-Marquardt method, adjusts the prediction covariance by suboptimal fading factor. A simplified calculation method of suboptimal fading factor is also presented. The previous process noise is suppressed by exponential fading factor, and the observation noise is corrected by the residual and innovation, so that filter gain becomes larger, which improves the accuracy and robustness of the filter algorithm. The experiment results indicate the improved algorithm can effectively suppress the filter divergence. Compared with the classical extended Kalman filter (EKF), IEKF, and the modified IEKF, the filter accuracy and robustness of the improved algorithm are sufficient, and the algorithm can better meet the requirements of engineering applications.