交互多模型PHD滤波跟踪多目标方法

Interactive Multi-Modle PHD Filter for Multi-Target Tracking

摘要:

在机动多目标跟踪问题中,目标数未知或随时间而变化,概率假设密度(PHD)滤波可以在每一时间步估计多目标状态和目标数,但单模型方法不能给出精确的估计。提出了一种交互多模型PHD滤波方法,建立多模型描述多目标运动方式,利用PHD滤波结合多模型跟踪目标运动轨迹。同时,给出了多传感器交互多模型PHD滤波方法,以提高目标跟踪精度。

Abstract:

In mameuvering multiple-target tracking problem, the number of targets was unknown or varied with time, probability hypothesis density(PHD) filter can be used to estimate the multi-target state and the number at each time step, but single model method may not provide accurate estimates. In this paper, a interactive multiple model PHD filter was proposed, and then multiple sensor interactive multiple model PHD filter was proposed to improve the tracking of multiple maneuvering targets. PHD particle filter implementation was used to perform the proposed method consisting of multiple mameuvering targets.