We present a modelling framework for multi-target tracking based on possibility theory and illustrate its ability to account for the general lack of knowledge that the target-tracking practitioner must deal with when working with real data. We also introduce and study variants of the notions of point process and intensity function, which lead to the derivation of an analogue of the probability hypothesis density (PHD) filter. The gains provided by the considered modelling framework in terms of flexibility lead to the loss of some of the abilities that the PHD filter possesses; in particular the estimation of the number of targets by integration of the intensity function. Yet, the proposed recursion displays a number of advantages such as facilitating the introduction of observation-driven birth schemes and the modelling the absence of information on the initial number of targets in the scene. The performance of the proposed approach is demonstrated on simulated data.

翻译：我们提出了一个基于可能性理论的多目标跟踪建模框架,并表明它有能力说明目标跟踪从业者在使用真实数据时必须处理的普遍缺乏知识的问题,我们还引入和研究点过程和强度功能概念的变体,从而得出概率假设密度过滤器的类似数据,经过考虑的建模框架在灵活性方面产生的收益导致PHD过滤器丧失一些能力,特别是通过结合强度功能估计目标数量,然而,拟议的回溯显示一些优势,例如便利采用观察驱动的分娩计划,以及模拟缺乏关于现场最初目标数量的信息,拟议方法的绩效在模拟数据上得到证明。