针对无迹卡尔曼滤波（unscented Kalman filter, UKF）中无迹变换（unscented transform, UT）在状态估计时采样点分布状态控制参数异常对滤波性能的影响问题，提出了一种利用多策略改进麻雀搜索算法（improved sparrow search algorithm, ISSA）对UT中采样点分布状态控制参数进行寻优调整的方法，从而优化Sigma点分布以提高非线性近似效果，改善滤波估计性能。同时针对传统麻雀搜索算法面临的易陷入局部最优和收敛速度慢等问题，首先利用Cubic混沌映射改善初始种群的多样性；其次在发现者阶段引入非线性自适应收敛因子，提高平衡算法在全局探索和局部开发方面的能力；同时在追随者阶段利用小波变异策略，以避免追随者盲目追随而导致算法陷入局部最优；最后利用自适应t分布的扰动能力增强算法的全局搜索能力。通过测试函数对ISSA算法进行仿真实验，结果表明ISSA算法具有更好的收敛性和求解精度，同时验证ISSA优化UKF算法后的仿真结果，表明了ISSA-UKF算法相比于UKF算法的位置均方根误差降低了52.2%，速度均方根误差降低了21.9%，证明了改进方法的有效性和可行性。

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) was introduced. The issue of abnormal filtering performance arising from the state of sample point distributions was addressed by this method. A multi-strategy improved sparrow search algorithm(ISSA) was 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 were implemented. Initially, a Cubic chaotic mapping was applied to diversify the initial population. Furthermore, during the exploration phase, a nonlinear adaptive convergence factor was introduced to balance the algorithm's capacity for global exploration and local exploitation. Additionally, a wavelet mutation strategy was 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 was introduced to strengthen the algorithm's ability to perform wide-ranging global searches. The efficacy of the proposed ISSA was 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.