论文标题
在汽车雷达传感器数据上进行多目标跟踪的轨道检测方法
A Track-Before-Detect Approach to Multi-Target Tracking on Automotive Radar Sensor Data
论文作者
论文摘要
近年来，在多目标跟踪域中，标记为随机有限设置公式的贝叶斯滤波器方法变得越来越强大。最新结果之一是通用标记的多重bernoulli（GLMB）过滤器，该过滤器允许在统一框架中进行稳定的基数和目标状态估计以及目标识别。与GLMB滤波器的初始上下文相反，本文在轨道前检测方案（TBD）方案中使用了它，因此避免了由于阈值和其他数据预处理步骤而引起的信息丢失。本文在可分离的可能性假设下提供了TBD GLMB滤波器设计，该假设可以应用于汽车雷达环境中的现实世界情景和数据。在模范场景中证明了它对真实传感器数据的适用性。据作者所知，首次将GLMB过滤器应用于TBD框架中的真实雷达数据。
In recent years, Bayes filter methods in the labeled random finite set formulation have become increasingly powerful in the multi-target tracking domain. One of the latest outcomes is the Generalized Labeled Multi-Bernoulli (GLMB) filter which allows for stable cardinality and target state estimation as well as target identification in a unified framework. In contrast to the initial context of the GLMB filter, this paper makes use of it in the Track-Before-Detect (TBD) scheme and thus, avoids information loss due to thresholding and other data preprocessing steps. This paper provides a TBD GLMB filter design under the separable likelihood assumption that can be applied to real world scenarios and data in the automotive radar context. Its applicability to real sensor data is demonstrated in an exemplary scenario. To the best of the authors' knowledge, the GLMB filter is applied to real radar data in a TBD framework for the first time.
