针对一般粒子滤波算法容易受到相似背景干扰和遮挡影响的问题,提出一种新的融合反馈的改进粒子滤波跟踪算法。该算法将最近的观测信息融入建议分布函数,便于粒子搜索目标最可能的位置,根据相对位移的变化自适应调整跟踪窗口尺度的变化,降低了计算的复杂度,一定程度上保持了粒子的多样性。实验结果表明：该算法有效地解决了遮挡、相似背景混乱以及目标尺寸变化问题,整体跟踪性能优于粒子滤波算法。

A new particle filter based on feedback fusion was proposed to solve the problem that the traditional particle filters were easily influenced by the similar background and occlusion. The proposal distribution was fused with the recent observation information,so that the particle could search for the most probable location of the object. According to the change of the relative displacement, the size of the tracking window was changing adaptively, which reduced the computational complexity and kept the diversity of the particles to a certain extent. The experimental results show that the proposed algorithm effectively deals with the occlusion, chaotic and similar background and change of the target size, providing better tracking performance than particle filter.