Performances in multitarget tracking for convoy detection over real GMTI data

Evangeline Pollard, B. Pannetier, M. Rombaut
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引用次数: 5

Abstract

A convoy is defined as a group of vehicles traveling together for mutual support and protection. It constitutes an object of high military interest in the context of the situation assessment. However, it is a challenging task to track and evaluate because convoy targets are very close of each other. In this view, the Onera recently developed a new two step convoy detection process. The first is an original tracking algorithm appropriate for Ground Moving Target Indicator (GMTI) data based on the hybridization of two classical multitarget tracking algorithms, and adapted to closely spaced target tracking. Then, by using algorithm outputs and other data, vehicle aggregates are detected and their characteristics are introduced into a Dynamic Bayesian Network (DBN) which processes the probability for an aggregate to be a convoy. This process gives encourageous results with simulated data. In this paper, we validate this process by showing real data results.
基于真实GMTI数据的车队多目标跟踪性能研究
车队的定义是一组车辆一起行驶,相互支持和保护。在局势评估的范围内,它是一个具有高度军事利益的对象。然而,跟踪和评估是一项具有挑战性的任务,因为车队目标彼此非常接近。鉴于此,Onera最近开发了一种新的两步车队检测流程。首先,在融合两种经典多目标跟踪算法的基础上,提出了一种适合于地面运动目标指示(GMTI)数据的原始跟踪算法,并适应于近距离目标跟踪。然后,利用算法输出和其他数据,检测车辆聚合体,并将其特征引入动态贝叶斯网络(DBN),该网络处理聚合体成为车队的概率。这一过程通过模拟数据得到了令人鼓舞的结果。在本文中,我们通过展示真实的数据结果来验证这一过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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