The probability hypothesis density (PHD) filter has been regarded as a practicable multi-target tracking algorithm which can alleviate the computational difficulty of the multi-target Bayes filter due to the multiple high-dimensional space integrals. As one of the major implementations of the PHD filter, sequential Monte Carlo PHD (SMC-PHD) is suitable for highly nonlinear systems. However, in scenarios with missing detections, the false-estimation problem occurs which leads to estimation performance degradation. To reduce the negative effects of missing detections, this paper develops a compensatory measurements generating mechanism and presents a novel measurement compensation based SMC-PHD filter, which can avoid the unreliable clustering. Comparative results verify the proposed filter, indicating good application prospects.

概率假设密度（PHD）滤波器被认为是一种可行的多目标跟踪算法，它可以缓解多目标贝叶斯滤波器由于多个高维空间积分的计算难度。作为PHD滤波器的主要实现之一，顺序蒙特卡罗PHD（SMC-PHD）适用于高度非线性系统。然而，在检测缺失的情况下，会出现误估计问题，导致估计性能下降。为了减少丢失检测的负面影响，本文开发了一种补偿测量生成机制，并提出了一种新的基于测量补偿的 SMC-PHD 滤波器，可以避免不可靠的聚类。对比结果验证了所提出的滤波器，表明具有良好的应用前景。