When using Unscented Kalman Filter (UKF) algorithm for state estimation, the abnormal system noise covariance matrix may affect the filtering performance. To address the problem, a method utilizing the Improved Artificial Bee Colony Optimized UKF (IABC-UKF) algorithm is proposed. Firstly, IABC is introduced into the UKF algorithm to optimize the selection of the system noise covariance matrix, thus to achieve adaptive adjustment of the system noise covariance matrix and improve estimation accuracy. Secondly, a Circle chaos initialization strategy is applied to the traditional ABC algorithm to increase the diversity of the initial population of artificial bee colony. Additionally, a preference random walk strategy is employed to balance the algorithm's exploitation and exploration capabilities, enhancing algorithm stability. Finally, a dynamic perturbation factor strategy is used to enhance the algorithm's ability to find the optimal solution in the later stages, improving convergence speed and further optimizing algorithm performance. Experimental results demonstrate that compared with the ABC algorithm, IABC algorithm has a significant improvement in optimization performance. Furthermore, a comparison between the UKF algorithm and the IABC-UKF algorithm confirms the feasibility of the IABC-UKF algorithm. With a root mean square error in position not exceeding 1.4 meters, IABC-UKF algorithm exhibits good filtering performance with low error fluctuations, which can effectively enhance the estimation accuracy.