Multitarget Tracking_Wiley_MTT_Preprint-revised.pdf

介绍多目标跟踪算法，可用于毫米波雷达目标的跟踪处理。可以基于位置、速度、角度等原始目标数据，通过处理跟踪和预测目标的轨迹。
Abstract—Multitarget tracking (MTT) refers to the problem
of jointly estimating the number of targets and their states or
trajectories from noisy sensor measurements. MTT has a long
history spanning over 50 years, with a plethora of applications
in many fields of study. While numerous techniques have been
developed, the three most widely used approaches to MTT are
the joint probabilistic data association filter (JPDAF), multiple
hypothesis tracking (MHT), and random finite set (RFS). The
JPDAF and MHT have been widely used for more than two
decades, while the random finite set (RFS) based MTT algorithms
have received a great deal of attention during the last decade.
In this article, we provide an overview of MTT and succinct
summaries of popular state-of-the-art MTT algorithms.