CAJ | 학술논문

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。
표준적대세개솔가설밀도（cardinalized probability hypothesis density，CPHD）려파기시일개유효적다목표근종산법，단시타가정신생목표적강도함수선험이지，인이무법응용우신생목표재장경중임의위치출현적배경。침대차문제，제출일충단보초시화적고사혼합 CPHD 려파기。해려파기이용위치상원리당전시각고계상태적관측치단보초시화신생목표。차외，다보륵신식일방면피용래초시화신생목표적속도，령일방면재려파기경신보취중，다보륵속도화위치관측신식채용천행경신방법처리。방진결과표명，소제산법재목표수적고계정도화우화자모식분배거리방면우우이유산법。
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 can-not work well in the situations where targets can appear anywhere in the surveillance region.To solve this prob-lem,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 posi-tion and Doppler measurements are incorporated in a serial process.Simulations show that the proposed algo-rithm 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.