The random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) filter is a promising and efficient suboptimal approximation for the multi-target Bayes filter. However, the GM-PHD filter is unable to track nearby targets caused by the improper position distribution of target-originated measurements. Aiming at the problem, a multi-target GM-PHD filter with an improved component merging method is proposed. Based on a proposed adaptive threshold-based component similarity measure scheme, the improved component merging method is able to avoid incorrect fusion of the components of targets in close proximity and optimize the target components within the target posterior intensity. Experimental results illustrate that the proposed algorithm not only can achieve better estimation accuracy in terms of the target states and its number but also has high computation efficiency when compared against the related GM-PHD-based filters.

基于随机有限集（RFS）的高斯混合概率假设密度（GM-PHD）滤波器是多目标贝叶斯滤波器的一种有前途且有效的次优近似。但是，GM-PHD滤波器无法跟踪由目标产生的测量值的位置分布不正确引起的附近目标。针对该问题，提出了一种具有改进的分量合并方法的多目标GM-PHD滤波器。基于提出的自适应基于阈值的成分相似性度量方案，改进的成分合并方法能够避免目标成分紧密融合的错误融合，并在目标后强度内优化目标成分。