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An ontology for active and passive aerial drone threat automatic plan recognition

机译：主动和被动空中无人机威胁自动计划识别的本体

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This paper initiates a discussion on the design of terms, features, and descriptors that would support machine learning for automated plan recognition of drone and drone swarms engaged in threatening activity. A few prototype aerial missions for drones are discussed and semantic markers, such as distance and line of sight to potential targets, mirrored motion, path and position optimality, coordination, and formation, are noted. This semantic description of motion in terms of objectives and capabilities contrasts with a naïve description of motion in a 3d coordinate system without reference to targets; terminology is the first step in automated anomaly detection analytics. The paper further discusses active plan recognition, which selects interventions in order to force the drone or swarm to reveal its intentions. Analogies to, and distinctions from, two-dimensional active plan discernment, e.g., stalking, tailing, pursuing, and intercepting, are given.
机译：本文就术语，特征和描述符的设计展开了讨论，这些术语，特征和描述符将支持机器学习以自动识别参与威胁活动的无人机和无人机群。讨论了一些无人机的原型空中任务，并指出了语义标记，例如到潜在目标的距离和视线，镜像运动，路径和位置的最优性，协调性和编队。就目标和能力而言，这种对运动的语义描述与不参考目标的3d坐标系中对朴素的运动的描述形成了对比。术语是自动异常检测分析的第一步。本文进一步讨论了主动计划识别，该计划选择干预措施以迫使无人机或蜂群揭示其意图。给出了与二维主动计划识别（例如跟踪，跟踪，追踪和拦截）的类比和区别。

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