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基于超宽带感知的先进信号处理方法的无人机运动检测与特征分析新方法。

New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB Sensing.

机构信息

Telecommunications and Information Technology Department, Military Technical Academy "Ferdinand I", 050141 Bucharest, Romania.

GIPSA-Lab, Université Grenoble Alpes, 38400 Saint Martin d'Hères, France.

出版信息

Sensors (Basel). 2020 Oct 19;20(20):5904. doi: 10.3390/s20205904.

DOI:10.3390/s20205904
PMID:33086724
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7589194/
Abstract

In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.

摘要

在过去几年中，商用无人机/无人驾驶飞行器市场由于其技术性能（由多种可用的机载传感器提供）、低价格和易用性而增长。由于事件数量的增加，它们的存在对具有特殊地位的公共或机密区域构成了一个主要问题，因为它们对越来越多的应用具有吸引力。我们的论文提出了一种基于超宽带 (UWB) 感测系统和先进信号处理方法的无人机运动检测和特征描述的新方法。该方法使用相关、包络检测、时频分析等经典方法以及一种新方法——递归图分析来描述无人机的运动。为了提供未来的无人机入侵检测起点，我们比较了这些方法在运动图精度和所需计算时间方面的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/78fd1666eb6e/sensors-20-05904-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/98ef7063b315/sensors-20-05904-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/b4caf727ef8a/sensors-20-05904-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/e3211d79291a/sensors-20-05904-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/a55fee1eae44/sensors-20-05904-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/fde744d233e1/sensors-20-05904-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/c3ca2083df69/sensors-20-05904-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/2201187533bc/sensors-20-05904-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/82677766a57a/sensors-20-05904-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/ed2fa8fe6502/sensors-20-05904-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/78fd1666eb6e/sensors-20-05904-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/98ef7063b315/sensors-20-05904-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/b4caf727ef8a/sensors-20-05904-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/e3211d79291a/sensors-20-05904-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/a55fee1eae44/sensors-20-05904-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/fde744d233e1/sensors-20-05904-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/c3ca2083df69/sensors-20-05904-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/2201187533bc/sensors-20-05904-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/82677766a57a/sensors-20-05904-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/ed2fa8fe6502/sensors-20-05904-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fdf/7589194/78fd1666eb6e/sensors-20-05904-g010.jpg

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