A Modified Progressive Extended Kalman Filter for Nonlinear Systems With Compound Noises

In nonlinear filtering, the performance of Kalman filters is highly dependent on the characteristics of the measurements. However, in practical applications, measurements contain different noise components due to complex factors, making the robustness and accuracy of the estimator not guaranteed. In this article, a modified progressive extended Kalman Filter (MPEKF) is presented for nonlinear systems with compound measurement noises. To enhance the robustness of the system, a hypothesis testing method is employed to identify and remove outliers. Subsequently, considering the problem of non‐orthogonality in the filtering process, a new set of measurement update equations is proposed to improve state estimation accuracy. Moreover, a termination condition is proposed to improve the computational efficiency and adaptability of the filter. Furthermore, through theoretical analysis of the MPEKF method, it is proven that the state estimation error remains bounded during the progressive process. Finally, the simulation of a nonlinear system example demonstrates that MPEKF has higher robustness and accuracy than EKF, IEKF, and PEKF.