针对复杂环境下因外界干扰产生时变非高斯噪声的情况下主从式无人机群协同导航效果较差的问题，提出了一种改进的粒子滤波（PF）算法以提高导航精度，并降低了对从机测量设备的精度要求。首先以主机的高精度惯性导航系统（INS）和全球定位系统（GPS）导航信息为基准，结合从机上的低精度传感器，建立了观测模型。其次，利用改进的PF算法实现了多源导航信息的融合。针对PF的重要性概率密度函数选取和粒子退化问题，在扩展粒子滤波（EPF）的基础上，引入Levenberg-Marquardt迭代方法，保证滤波的稳定性和收敛性。在重采样阶段采用快速重采样方法，将得到的粒子集进行分类，对中等权重粒子不再进行重采样，其余粒子在归一化过程中利用自适应权重因子优化，使获得的样本粒子权重更加均匀，从而提高了计算效率和导航实时性。将提出的方法与其他几种PF进行对比，仿真结果表明，该算法可以有效地提高无人机（UAV）编队协同导航精度，具有一定实用价值。

To address the problem of poor cooperative navigation of master-slave UAV swarms in complex environments with time-varying non-Gaussian noise due to external interference， an improved Particle Filter （PF） algorithm is proposed to improve navigation accuracy and reduce the accuracy requirements of the slave measurement equipment. Firstly， the observation model is established by taking the high-precision Inertial Navigation System （INS） and Global Position System （GPS） navigation information from the host machine as the reference and combining it with the low-precision sensors on the slave machine. Then， the fusion of multi-source navigation information is achieved using an improved PF algorithm. For the importance probability density function selection and particle degradation problems of PF， the Levenberg-Marquardt iterative method is introduced on the basis of Extended Particle Filter （EPF） to ensure the stability and convergence of the filter. In the resampling phase， the fast resampling method is proposed to classify the obtained particle set， and the medium weight particles are not resampled anymore， while the rest of the particles are optimized in the normalization process using adaptive weight factors to make the obtained sample particle weights more uniform， thus improving the computational efficiency and navigation real-time. The simulation results show that compared with other PFs， the proposed algorithm can effectively improve the accuracy of Unmanned Aerial Vehicle （UAV） formation cooperative navigation and has certain practical value.