关键词: 多目标跟踪, 随机集, 混合高斯, 概率假设密度

Abstract: When the number of targets is unknown or varied with time，the target state and measurements can be represented as random sets.The Gaussian mixture Probability Hypothesis Density（PHD） filter is implemented to track the multi-targets.The analytical analysis of the method show that the posterior intensity at any subsequent time step remains a Gaussian mixture under the assumption that the state noise，the measurement noise，target spawn intensity，new birth intensity，target survival probability，and detection probability are all Gaussian mixture.The Kalman filter is embedded in the Gaussian mixture PHD filter.This method uses Gaussian components to predict and update the PHD of random sets，and estimates targets states.Experiments show that the Gaussian mixture PHD filter can be used to track multi-target in clutter effectively.

Key words: multi-target tracking, random set, Gaussian mixture, probability hypothesis density