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基于拟蒙特卡罗方法的概率假设密度多目标跟踪

, PP. 1221-1225

Keywords: 多目标跟踪,概率假设密度,拟蒙特卡罗方法,低偏差点集

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Abstract:

为了改善多目标跟踪问题中概率假设密度(PHD)滤波的估计精度,提出基于拟蒙特卡罗的PHD滤波算法.该算法利用低偏差点集在状态空间中分布均匀的特性,使得采样粒子集最大程度地相互远离,充分地描述多目标状态的后验概率密度,从而准确地利用带有相应权值的粒子集来计算多目标数目和各个目标状态的估计值.仿真实验表明了算法的有效性,且估计性能优于粒子PHD滤波算法.

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