在多目标跟踪中,在观测数据存在关联的不确定、检测的不确定、噪声和虚警情形下,同时估计出随时间变化的目标数及目标状态,高斯混合概率假设密度(GMPHD)提供了一种有效的方法。PHD滤波不存在解析解,而GMPHD滤波提供了PHD递推的解析解。仿真结果表明,GMPHD滤波能稳健的跟踪目标数未知或时间变化时的目标状态和目标数。

In multi-target tracking problem, not only the states of targets, the time varying number of targets, but also a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms should be estimated. The Gaussian mixture probability hypothesis density (GMPHD) filter offered an effective method for multi-target tracking. Due to the PHD propagation equations involved multiple integrals; there were no computationally tractable closed form expressions. Fortunately, the GMP HD filter provided a closed form solution to the PHD filter recursion. The posterior intensity function was estimated by a sum of weighted Gaussian components whose means, weights and covariances can be propagated analytically in time. Experiments show that the GMPHD filter can track a changing number of targets robustly, achieving near-real-time performance.