CAJ | 학술논문

多传感器多目标跟踪是天基红外系统的核心关键技术之一，面临目标数目随时间变化、目标出现时间与位置难以预知、虚警干扰严重等难点，传统多目标跟踪方法存在组合爆炸问题。文中采用高斯混合概率假设密度滤波器（GM-PHD， Gaussian mixture probability hypothesis density）进行多目标跟踪，有效避免了复杂的数据关联运算；同时，针对GM-PHD滤波器新目标丢失问题，采用反馈滤波方法，通过将传感器量测假设作为新目标的量测，有效实现了对新目标的快速跟踪处理。仿真试验结果表明，本文所提反馈滤波方法具有更好的跟踪性能和更快的收敛速度。
다전감기다목표근종시천기홍외계통적핵심관건기술지일，면림목표수목수시간변화、목표출현시간여위치난이예지、허경간우엄중등난점，전통다목표근종방법존재조합폭작문제。문중채용고사혼합개솔가설밀도려파기（GM-PHD， Gaussian mixture probability hypothesis density）진행다목표근종，유효피면료복잡적수거관련운산；동시，침대GM-PHD려파기신목표주실문제，채용반궤려파방법，통과장전감기량측가설작위신목표적량측，유효실현료대신목표적쾌속근종처리。방진시험결과표명，본문소제반궤려파방법구유경호적근종성능화경쾌적수렴속도。
The Gaussian mixture probability hypothesis density (GM-PHD) filter was proposed for jointly estimating the time-varying number of targets and their states from a sequence of sets of measurements. However, when the intensities of newly birthed targets are unknown, it is hard to detect them quickly. To solve this problem, an approach using historic measurements is proposed. Simulation results show that the proposed algorithm has an equivalent performance with algorithms with prior intensities of birthed targets.