概率假设密度（probability hypothesis density，PHD）滤波的序贯蒙特卡罗实现算法性能高度依赖于先验目标生成强度函数和粒子重要性采样（importance sampling，IS）函数。针对上述问题，提出一种改进算法。首先，引入量测驱动机制，提出一种量测分类方法获取潜在的新生目标量测集合，并以此为基础进行新生目标粒子采样，提高了算法的有效性。其次，为了提高存活目标粒子分布的准确性，结合门技术和无迹信息滤波将当前量测信息融入到 IS 函数设计中。计算机仿真实验表明，所提算法具有更稳健的多目标跟踪能力和杂波适应性。%The performance of probability hypothesis density (PHD)filter depends heavily on the priori of birth target intensity and the selection of importance sampling (IS)function when the sequential Monte Carlo method is used to implement it.To solve these problems,an improved algorithm is proposed.Firstly,a meas-urement-driven mechanism is introduced to classify the measurements to get the birth measurements which are used for exploring newborn targets.Secondly,the unscented information filtering is used to incorporate the cur-rent measurements information into the IS function,combined with the gate technique to choose the measure-ments matching with the persistent targets.The results of computer simulation indicate that the proposed algo-rithm outperforms similar algorithms in its ability to operate in clutter,and can initiate and estimate targets more accurately.
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