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Nonlinear filtering methods for the state estimation of dynamic systems and applications in navigation https://doi.org/10.1063/5.0107936статья
- Авторы: Shen Xin, Lukyanov Vadim
- Сборник: XLV ACADEMIC SPACE CONFERENCE, DEDICATED TO THE MEMORY OF ACADEMICIAN S.P. KOROLEV AND OTHER OUTSTANDING NATIONAL SCIENTISTS — PIONEERS OF SPACE EXPLORATION
- Год издания: 2023
- Издательство: OAE Publishing Inc.
- Местоположение издательства: United States
- Аннотация: The paper investigates new nonlinear filters that allow for more efficient fusion of data from various types of sensors and provide better accuracy and stability in the generation of navigation parameters with a lower computational load. There are quite a lot of different types of nonlinear filters, they are addressed and discussed in detail. In the past, the extended Kalman filter (EKF) is based on the calculation of the derivation and the process of the Jacobian matrix, which has a large computational burden for the computer processor. Then a derivative-free numerical approximation-based Gaussian filter, named the unscented Kalman filter (UKF), was introduced in the nineties, which offered several advantages over the traditional derivative-based Gaussian filters. Since the proposition of UKF and then cubature Kalman filter (CKF), derivative-free Gaussian filtering has received more and more attention from the researchers all over the world. With the improvement of matrix decomposition related theories, square-root cubature Kalman filter (SCKF) has also been developed on the basis of CKF. The characteristics and performance of the filters are analyzed by simulation of a tightly coupled integrated inertial-satellite navigation system. The proposed filter demonstrates better performance in terms of estimation error and rate of convergence than the traditional ones in high dimensional navigation system.
- Добавил в систему: Шэнь Синь