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无人机探测与防御系统:综述与基于软件无线电的解决方案。

Drone Detection and Defense Systems: Survey and a Software-Defined Radio-Based Solution.

机构信息

Telecommunications Department, University Politehnica of Bucharest, 060042 Bucharest, Romania.

出版信息

Sensors (Basel). 2022 Feb 14;22(4):1453. doi: 10.3390/s22041453.

DOI:10.3390/s22041453
PMID:35214355
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8879497/
Abstract

With the decrease in the cost and size of drones in recent years, their number has also increased exponentially. As such, the concerns regarding security aspects that are raised by their presence are also becoming more serious. The necessity of designing and implementing systems that are able to detect and provide defense actions against such threats has become apparent. In this paper, we perform a survey regarding the different drone detection and defense systems that were proposed in the literature, based on different types of methods (i.e., radio frequency (RF), acoustical, optical, radar, etc.), with an emphasis on RF-based systems implemented using software-defined radio (SDR) platforms. We have followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines in order to provide a concise and thorough presentation of the current status of the subject. In the final part, we also describe our own solution that was designed and implemented in the framework of the DronEnd research project. The DronEnd system is based on RF methods and uses SDR platforms as the main hardware elements.

摘要

近年来,随着无人机成本和尺寸的降低,其数量也呈指数级增长。因此,人们对其存在所引发的安全问题的担忧也变得更加严重。设计和实施能够检测和提供防御措施以应对这些威胁的系统变得非常必要。在本文中,我们根据不同的方法(例如射频 (RF)、声学、光学、雷达等)对文献中提出的不同的无人机检测和防御系统进行了调查,重点介绍了使用软件定义无线电 (SDR) 平台实现的基于 RF 的系统。我们遵循了系统评价和荟萃分析的首选报告项目 (PRISMA) 指南,以便简洁而全面地呈现该主题的现状。在最后一部分,我们还描述了我们自己的解决方案,该解决方案是在 DronEnd 研究项目的框架内设计和实现的。DronEnd 系统基于 RF 方法,并使用 SDR 平台作为主要硬件元素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/c081f7516d5a/sensors-22-01453-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/12b180844dfc/sensors-22-01453-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/0e13d3d42fb5/sensors-22-01453-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/49e01abfee7a/sensors-22-01453-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/cac22ac184ed/sensors-22-01453-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/4e366d7372c1/sensors-22-01453-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/c081f7516d5a/sensors-22-01453-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/12b180844dfc/sensors-22-01453-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/2113e2bbac77/sensors-22-01453-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/0e13d3d42fb5/sensors-22-01453-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/49e01abfee7a/sensors-22-01453-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/cac22ac184ed/sensors-22-01453-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/4e366d7372c1/sensors-22-01453-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac9f/8879497/c081f7516d5a/sensors-22-01453-g007.jpg

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3
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5
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Sensors (Basel). 2023 Jun 29;23(13):6037. doi: 10.3390/s23136037.
6
UWB Sensing for UAV and Human Comparative Movement Characterization.超宽带(UWB)感测在无人机和人类比较运动特征化中的应用。
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7
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9
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10
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4
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7
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8
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