Abstract

This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.

中文翻译：

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HMM使用声学圆形麦克风阵列波束成形对无人机进行分类，定位和跟踪

摘要

本文解决了识别和跟踪非法无人机的监控系统的问题。无人机技术的发展促进了无人机的广泛商业应用。但是，无人机携带炸药和其他破坏性材料的能力可能会严重威胁公共安全。为了减少这些威胁，我们提出了一种基于声学的方案来定位和跟踪非法无人机。我们提出的方案有三个主要重点。首先，我们使用切换波束成形扫描天空，找到声源并使用麦克风阵列记录声音；其次，我们使用隐马尔可夫模型（HMM）进行分类，以了解声音是无人机还是其他东西。最后，如果声源是无人机，我们将其记录的声音用作基于自适应波束成形的跟踪参考信号。仿真是在理想条件下（没有背景噪声和干扰声）和非理想条件下（有背景噪声和干扰声）进行的，我们在跟踪非法无人机时会评估性能。