high performance hybrid robust extended kalman filter design with application to large misalignments

In many applications, especially military applications, the inertial navigation system (ins) needs to achieve a high level of accuracy in a short time. for alignment, recursive estimator filters and, in non-linear cases, the extended kalman filter (ekf) is often used. the dynamics of a real, continuous system and the output of the sensors are available discretely. therefore, a hybrid filter has been used. in addition, a robust filter is used to increase the reliability of system operation. in this paper, a hybrid extended kalman filter (hekf) is presented and then upgraded to the hybrid robust extended kalman filter (hrekf). by running the algorithm on the data of a real system, it was observed that the speed of convergence increased especially in the yaw direction. by running the algorithm on the data of a real system, it was observed that the speed of convergence has increased especially in the yaw direction. finally, using the impulsive system approach, a new stability analysis of the proposed algorithms is presented, which guarantees the boundedness of the error estimation, which is unique.

high performance hybrid robust extended kalman filter design with application to large misalignments

in many applications, especially military applications, the inertial navigation system (ins) needs to achieve a high level of accuracy in a short time. for alignment, recursive estimator filters and, in non-linear cases, the extended kalman filter (ekf) is often used. the dynamics of a real, continuous system and the output of the sensors are available discretely. therefore, a hybrid filter has been used. in addition, a robust filter is used to increase the reliability of system operation. in this paper, a hybrid extended kalman filter (hekf) is presented and then upgraded to the hybrid robust extended kalman filter (hrekf). by running the algorithm on the data of a real system, it was observed that the speed of convergence increased especially in the yaw direction. by running the algorithm on the data of a real system, it was observed that the speed of convergence has increased especially in the yaw direction. finally, using the impulsive system approach, a new stability analysis of the proposed algorithms is presented, which guarantees the boundedness of the error estimation, which is unique.