CAJ | 학술논문

针对多机动目标跟踪中采用统一固定模型转移概率的问题,提出一种在线估计模型转移概率的自适应多模型PHD滤波( AIMM-PHD)。首先保留模型的采样粒子及其似然度；其次根据粒子的分类结果,计算出每个目标对应每个模型的状态输出；最后将输出交替作为模型输入进行滤波,计算出目标的模型转移概率。实验表明：相较于IMM-PHD,所提AIMM-PHD有较低的OSPA误差,目标个数估计更准确,且时间只增加了8.1%,从而证明了该算法的有效性。
침대다궤동목표근종중채용통일고정모형전이개솔적문제,제출일충재선고계모형전이개솔적자괄응다모형PHD려파( AIMM-PHD)。수선보류모형적채양입자급기사연도；기차근거입자적분류결과,계산출매개목표대응매개모형적상태수출；최후장수출교체작위모형수입진행려파,계산출목표적모형전이개솔。실험표명：상교우IMM-PHD,소제AIMM-PHD유교저적OSPA오차,목표개수고계경준학,차시간지증가료8.1%,종이증명료해산법적유효성。
To solve the problem that multiple model probability hypothesis density ( IMM-PHD) filter for maneuvering target tracking uses the prior model transition probability, a adaptive algorithm to Markova transition probability proposed. Firstly, the particles and the likeli-hood every model in the process of particles interaction, and then the output of every model to every target according to assortment in the process of state estimation, lastly, the model transtions probability by Bayes principle. The results show:compared IMM-PHD, AIMM-PHD has lower OSPA error;higher accuracy of target number estimation but its time only increases 8. 1%, thus the effectiveness of the proposed algorithm.