基于随机有限集的UPF-CPHD多目标跟踪

提出一种基于随机有限集的无迹粒子基数概率假设密度滤波（UPF-CPHD, unscented particle filter - cardinality probability hypothesis density）的多目标跟踪方法。在粒子滤波框架下采用随机有限集（RFS, random finite sets）对多目标状态和观测进行描述。在UPF滤波框架下引入CPHD算法同时递推目标状态和目标数目，并计算最新观测信息，估计结果更加精确，弥补PHD估计目标数目不可靠的缺点。仿真实验表明，UPF-CPHD多目标跟踪方法能够降低超过50%的目标数目估计误差，并提高目标状态的估计精度。

Random finite sets based UPF-CPHD multi-object tracking

A multiple tracking method based on UPF-CPHD was proposed,in which the state and observation of the object were both described by the random finite sets (RSF).The CPHD algorithm was also introduced into the UPF framework to simultaneously deduce the object state and object number,making the estimation more precise.The experimental results show that the proposed UPF-CPHD algorithm is able to improve the estimation accuracy of the object number and state,as well as enhance the object tracking results.