基于迭代sigma点粒子滤波的再入目标跟踪

标准粒子滤波提议分布选择时,由于没有计入最近的观测值信息,重要性权的方差随时间递增,导致权值蜕化。针对这一问题提出了一种新的滤波算法,迭代sigma点粒子滤波算法。该算法在预测时采用sigma点粒子滤波产生拟合概率密度函数的加权粒子,并通过观测值对加权粒子进行更新;修正过程采用迭代卡尔曼滤波优化预测阶段得到的描述状态分布的均值和方差。将其运用于再入大气层目标的跟踪模型,仿真结果表明:与标准粒子滤波相比,该算法能保证滤波收敛,具有更高的估计精度和更好的鲁棒性。

Iterative sigma point particle filter in target tracking on reentry

In standard particle filter, proposal distribution is chosen without considering the most recent observation, so the variance of important weight increases with time, which results in weight degeneracy. To overcome this shortcoming, a new algorithm named the iterative sigma- point filter is proposed. In the prediction stage, weighted particles are drawn from the probability density function by the sigma-point filter. Then according to the observation data the weighted particles are updated. In the modification stage, iterative Kalman filter is adopted to optimize the mean and variance of the state distribution which are obtained in the prediction stage. Simulation of target tracking on reentry shows that this new algorithm can ensure the filter convergence. It also improves the estimation accuracy and robustness in comparison with the standard particle filter.