多目标、多传感器舰船跟踪与分类

Leonard Kosta, John Irvine, Laura Seaman, H. Xi
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引用次数: 0

摘要

美国国防部高级研究计划局(DARPA)等政府机构希望了解在具有战略意义的海洋区域航行的船只的数量、位置、轨迹和类型。我们实现了一种多重假设检验算法,利用来自多个传感器的经纬度数据同时跟踪数十艘船舶,然后结合使用行为指纹和深度学习技术,按类型对每艘船舶进行分类。目标的数量是先验未知的。我们在多个数据集上实现了高轨道纯度和高分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Many-target, Many-sensor Ship Tracking and Classification
Government agencies such as DARPA wish to know the numbers, locations, tracks, and types of vessels moving through strategically important regions of the ocean. We implement a multiple hypothesis testing algorithm to simultaneously track dozens of ships with longitude and latitude data from many sensors, then use a combination of behavioral fingerprinting and deep learning techniques to classify each vessel by type. The number of targets is unknown a priori. We achieve both high track purity and high classification accuracy on several datasets.
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