中文摘要：

状态跟踪测量的过程噪声降低了目标信噪比，增加了自适应滤波跟踪的难度。当误差较大时，基本粒子滤波算法的预测累积误差效应将导致系统发散。无迹粒子滤波算法利用无迹卡尔曼滤波提高重要性函数估计精度，减少后验概率密度分布误差，但同时也将大幅增加运算时间。提出一种基于径向基函数网络（RBFN）的改进型粒子滤波算法PF-RBF，利用RBFN通过目标状态观测值和全局预测值拟合状态变化趋势，更新各粒子状态，提高先验概率密度分布估计精度，消除过程噪声引起的估计误差。与无迹粒子滤波（UPF）算法相比，该算法无需构造无迹卡尔曼滤波砌重要性函数，提高了运算速度。机动目标跟踪试验表明，径向基粒子滤波算法在线性和非线性观测方程下的状态跟踪测量精度和算法稳定性均优于UKF、PF和UPF算法，可有效实现对状态变化的实时鲁棒跟踪。当参与运算的粒子数增加时，PF-RBF算法执行时间的增长速率较UPF算法更低，可满足高精度状态跟踪应用。

英文摘要：

In statement tracking, drastic change increases the process noise and accordingly increases the difficulty of self-adaptive filter tracking. Traditional particle filter algorithm has a disadvantage that if change is too drastic, it can not correct errors effectively, which makes the estimation errors cumulate and the tracking system become divergent Unscented particle filter （UPF） algorithm, which uses an unscented Kalman filter （UKF） for proposal distribution generation within a particle filter framework, can decrease the posterior probability distribution estimation error, enhance tracking effect, but it also increase the computation time. An improved particle filter algorithm（PF-RBF） based on radial basis function network （RBFN） is proposed, which aims at improving the sampling process of new particles and reducing the computation time. The algorithm uses RBFN to construct the process model dynamically from the observations and update the state of the system, which can reduce prior probability distribution estimation error and remove the cumulated effect of errors. Compared with UPF, PF-RBF can reduce computation time because it doesn＇t contain UKF process. The target tracking experiment results verify that PF-RBF performs better than UKF, PF and UPF whether the observation model is nonlinear or linear. Furthermore, the intrinsic property of PF-RBF determines that the change rate of execution time of PF-RBF is less than UPF, so PF-RBF is more suitable for large-scale applications.