A method for optimizing the control parameters of the sample point distribution state within the framework of the Unscented Transform (UT) for the Unscented Kalman Filter (UKF) is introduced. The issue of abnormal filtering performance arising from the state of sample point distributions is addressed by this method. A multi-strategy fused Sparrow Search Algorithm (ISSA) is employed to finely tune the control parameters. The goal is to enhance the distribution of Sigma points, thereby improving the effectiveness of nonlinear approximations and ultimately enhancing the accuracy of filtering estimations. To address the shortcomings of traditional Sparrow Search Algorithms, several refinements are implemented. Initially, a Cubic chaotic mapping is applied to diversify the initial population. Furthermore, during the exploration phase, a nonlinear adaptive convergence factor is introduced to balance the algorithm's capacity for global exploration and local exploitation. Additionally, a wavelet mutation strategy is integrated into the follower phase to prevent blind adherence to specific paths and mitigate the risk of becoming trapped in local optima. Lastly, an adaptive t-distribution perturbation capability is introduced to strengthen the algorithm's ability to perform wide-ranging global searches. The efficacy of the proposed Improved Sparrow Search Algorithm (ISSA) is demonstrated through simulation experiments conducted on various test functions. The results consistently show that ISSA outperforms other methods in terms of convergence and solution accuracy. Furthermore, the benefits of ISSA are extended to the optimization of parameters within the UKF algorithm. Experimental outcomes indicate that the ISSA-UKF algorithm reduces the Root Mean Square Error (RMSE) of position by 52.2% and the RMSE of velocity by 21.9%, thus affirming the viability and effectiveness of the proposed enhancements.