Passive radio system for real-time drone detection and DoA estimation

Basak, Sanjoy; Scheers, Bart

doi:10.1109/icmcis.2018.8398721

Cited by 22 publications

(13 citation statements)

References 6 publications

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“…Estimation by ToF/ToA and TDoA-based methods may be infeasible for the localization of uncooperative UAVs, as they require a common clock and synchronization. In one study [180], radio-based UAV detection and AoA estimation algorithms were investigated. In another study [181], AoA estimation techniques using a directional antenna array were proposed to localize UAVs.…”

Section: ) Orchestrationmentioning

confidence: 99%

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

et al. 2020

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Recognizing the various and broad range of applications of unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UAS) for personal, public and military applications, recent un-intentional malfunctions of uncontrollable UAVs or intentional attacks on them divert our attention and motivate us to devise a protection system, referred to as a counter UAV system (CUS). The CUS, also known as a counter-drone system, protects personal, commercial, public, and military facilities and areas from uncontrollable and belligerent UAVs by neutralizing or destroying them. This paper provides a comprehensive survey of the CUS to describe the key technologies of the CUS and provide sufficient information with wich to comprehend this system. The first part starts with an introduction of general UAVs and the concept of the CUS. In the second part, we provide an extensive survey of the CUS through a top-down approach: i) the platform of CUS including ground and sky platforms and related networks; ii) the architecture of the CUS consisting of sensing systems, command-and-control (C2) systems, and mitigation systems; and iii) the devices and functions with the sensors for detection-and-identification and localization-and-tracking actions and mitigators for neutralization. The last part is devoted to a survey of the CUS market with relevant challenges and future visions. From the CUS market survey, potential readers can identify the major players in a CUS industry and obtain information with which to develop the CUS industry. A broad understanding gained from the survey overall will assist with the design of a holistic CUS and inspire cross-domain research across physical layer designs in wireless communications, CUS network designs, control theory, mechanics, and computer science, to enhance counter UAV techniques further.INDEX TERMS Unmanned aerial vehicle (UAV), unmanned aircraft system (UAS), counter UAV system (CUS), counter drone systems, public safety, defense.

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Section: ) Orchestrationmentioning

confidence: 99%

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

et al. 2020

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“…Localization is also an important part of passive RF based UAS detection. In [45], the authors divided the complete detection procedure for UAS detection into RF spectrum sensing and the Direction of Arrival (DoA) estimated. [46] presented a useful experiment, which used commercial off the shelf Field Programmable Gate Array (FPGA) based SDR system for detecting and locating small UAS.…”

Section: A Acoustic Based Uas Detectionmentioning

confidence: 99%

Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends

Wang,

Liu,

Song

2020

Preprint

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Unmanned aircraft systems (UAS), or unmanned aerial vehicles (UAVs), often referred to as drones, have been experiencing healthy growth in the United States and around the world. The positive uses of UAS have the potential to save lives, increase safety and efficiency, and enable more effective science and engineering research. However, UAS are subject to threats stemming from increasing reliance on computer and communication technologies, which place public safety, national security, and individual privacy at risk. To promote safe, secure and privacyrespecting UAS operations, there is an urgent need for innovative technologies for detecting, tracking, identifying and mitigating UAS. A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or unmanned aircraft system. Over the past 5 years, significant research efforts have been made to detect, and mitigate UAS: detection technologies are based on acoustic, vision, passive radio frequency, radar, and data fusion; and mitigation technologies include physical capture or jamming. In this paper, we provide a comprehensive survey of existing literature in the area of C-UAS, identify the challenges in countering unauthorized or unsafe UAS, and evaluate the trends of detection and mitigation for protecting against UASbased threats. The objective of this survey paper is to present a systematic introduction of C-UAS technologies, thus fostering a research community committed to the safe integration of UAS into the airspace system.

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“…We presented an RF-based drone detection using GoF spectrum sensing and DoA estimation using MUSIC algorithm in [22]. Drone signal detection using wideband CFARbased energy detection and the feature extraction performance is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Combined RF-Based Drone Detection and Classification

Basak

Rajendran

Pollin

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

Self Cite

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Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats to security-sensitive places like airports and nuclear power plants. The existing drone localization and neutralization technologies work on the assumption that the drone has already been detected and classified. Although we have observed a tremendous advancement in the sensor industry in this decade, there is no robust drone detection and classification method proposed in the literature yet. This paper focuses on radio frequency (RF) based drone detection and classification using the frequency signature of the transmitted signal. We have created a novel drone RF dataset using commercial drones and presented a detailed comparison between a two-stage and combined detection and classification framework. The detection and classification performance of both frameworks are presented for a single-signal and simultaneous multi-signal scenario. With detailed analysis, we show that You Only Look Once (YOLO) framework provides better detection performance compared to the Goodness-of-Fit (GoF) spectrum sensing for a simultaneous multi-signal scenario and good classification performance comparable to Deep Residual Neural Network (DRNN) framework.

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Passive radio system for real-time drone detection and DoA estimation

Cited by 22 publications

References 6 publications

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends

Combined RF-Based Drone Detection and Classification

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