未知杂波下多目标跟踪AEM-PHD平滑滤波算法

Multi-target Tracking AEM-PHD Smoothing Filter Algorithm Under Unknown Clutter

针对未知杂波强度下的多目标跟踪问题,提出了加速期望最大化概率假设密度(AEM-PHD)平滑滤波算法。首先,对杂波的强度进行建模;接着,根据杂波的量测估计出杂波的个数;然后,利用高斯有限混合模型对杂波密度函数进行建模,在EM算法的基础上提出了AEM算法,将AEM算法用于高斯有限混合模型参数的估计,获得了杂波的密度函数;最后,将估计的杂波信息应用于多目标跟踪,对目标状态进行了平滑。仿真结果表明,在杂波强度未知的环境下,所提算法能准确估计出杂波的参数,具有跟踪精度高、目标数目估计准确的优点。

Aiming at the muhi-target tracking with unknown clutter intensity, we proposed an Accelerated Expectation Maximization Probability Hypothesis Density （AEM-PHD） smoothing filter algorithm. Firstly, the model of clutter intensity was established, and the number of clutters was estimated according to clutter measurements. Then, the clutter density function was modeled by using Gaussian finite mixture model. AEM algorithm was proposed on the basis of EM algorithm, which was used for estimating the parameters of the Gaussian finite mixture model, and the clutter density function was obtained. Finally, the estimated clutter information was applied to multi-target tracking, and the target states were smoothed. Simulation results showed that, under clutter with unknown intensity, the proposed method can estimate clutter parameters accurately with high target tracking precision and accurate estimation of the target number.