利用多普勒信息的单步初始化GMCPHD滤波器

标准的带势概率假设密度(cardinalized probability hypothesis density, CPHD)滤波器是一个有效的多目标跟踪算法,但是它假定新生目标的强度函数先验已知，因而无法应用于新生目标在场景中任意位置出现的环境。针对此问题，提出一种单步初始化的高斯混合CPHD滤波器。该滤波器利用位置上远离当前时刻估计状态的观测值单步初始化新生目标。此外，多普勒信息一方面被用来初始化新生目标的速度，另一方面在滤波器更新步骤中，多普勒速度和位置观测信息采用串行更新方法处理。仿真结果表明，所提算法在目标数的估计精度和优化子模式分配距离方面优于已有算法。

One-step initializing Gaussian mixture CPHD filter using Doppler information

The standard cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However,due to its assumption that the target birth intensity is known a priori，it cannot work well in the situations where targets can appear anywhere in the surveillance region. To solve this problem,a one step initializing Gaussian mixture CPHD (GMCPHD) filter is proposed to adaptively initialize the newborn targets using the measurements far away from the current estimated multi target states. Furthermore,Doppler information (DI) is used to initialize the velocities of the newborn targets,and in the update step position and Doppler measurements are incorporated in a serial process. Simulations show that the proposed algorithm can effectively initialize the newborn targets and improve the accuracy of target number estimation as well as the optimal subpattern assignment distance when compared with the existing algorithm.