基于CKF的最大似然误差配准算法OA

The traditional maximum likelihood registration(MLR)algorithm estimates the target state by directly projecting noisy measurements into the target state space after correction with the estimated systematic deviation.However,such algorithms ig-nore the influence of random measurement noise on the estimation performance.To address this problem,a target state estimation al-gorithm based on Cubature Kalman Filter(CKF)is proposed.The algorithm adopts CKF to realize real-time target state estimation,and improves error registration efficiency via iterative fusion,thus enhancing the fusion accuracy.The effectiveness,robustness and superiority of the proposed algorithm are verified by simulation analysis of the impacts of different random measurement noise and different systematic deviations on target state estimation,as well as the comparison of target state estimation performance of different algorithms.