Multiple clusters of particles characterized in terms of fixed-position and fixed-distribution are utilized for target capture, target state estimation, and track maintenance in traditional particle filters. It is often unsustainable in target acquisition and track maintenance since the prior information of target is hard to acquire. In addition, the obtained measurement information may be fuzzy (interval measurement) in practical engineering applications. This is also a challenge in traditional particle filters. In view of these two problems, a novel measurement-driven AParticle-δ-GLMB-IM filter is proposed. The AParticle-δ-GLMB-IM filter uses RLF to determine the affiliation between the survival targets and the interval measurements. A newborn label or a survival label will be given to the newborn particles driven by the same single interval measurement. Furthermore, this paper presents a new box particle application named ABox-δ-GLMB filter. Numerical experiments verify the effectiveness and superiority of the proposed methods.

利用固定位置和固定分布表征的多个粒子簇可用于传统粒子过滤器中的目标捕获，目标状态估计和跟踪维护。由于难以获取目标的先验信息，因此在目标获取和跟踪维护中通常是不可持续的。另外，在实际工程应用中，获得的测量信息可以是模糊的（间隔测量）。这在传统的颗粒过滤器中也是一个挑战。针对这两个问题，提出了一种新型的测量驱动的AParticle- δ -GLMB-IM滤波器。AParticle- δ-GLMB-IM过滤器使用RLF来确定生存目标和间隔测量值之间的联系。新生儿标签或生存标签将给予通过相同的单间隔测量驱动的新生儿颗粒。此外，本文提出了一种名为ABox- δ- GLMB过滤器的新盒粒子应用程序。数值实验验证了所提方法的有效性和优越性。