A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications

Flórez, Jimmy; Pabón, José David Ortega; Betancourt, Andrés; Garcia, A. Manjarres; Bedoya, Marlon; Botero, Juan Sebastián

doi:10.22430/22565337.1408

Cited by 23 publications

(9 citation statements)

References 48 publications

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“…Flórez et al 32 . review the state‐of‐the‐art techniques, methods, and algorithms used in video, RF, and audio‐based DDS.…”

Section: The Survey and Measures For Analysismentioning

confidence: 99%

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

Kashi,

Prashanth,

Kashi

et al. 2023

Systems Engineering

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The proliferation of drones in civil and military applications has brought about new challenges in the context of safety and security considerations of operational aspects. The use of small‐ and medium‐sized drones in civilian airspaces even with regulatory approval may not provide guarantees of safety and security. Detection of drones is the first step towards providing a mitigating mechanism for challenges posed by mal‐intentioned drones. Sensors and their characteristics affecting the detection process are discussed. A figure of merit is proposed to compare systems employing these sensors. The key focus of the paper is the examination of the spectrum of drone detection systems (DDS) using a systems‐based approach applied to scenarios. In order to make an equitable comparison between systems, two scenarios are utilized to discuss system characteristics. Next, a survey of DDS in each of these scenarios is performed. Artifacts are analyzed, culling information using the systems model. An approach is proposed to perform evaluation of these artifacts using a scoring mechanism. Simple yet novel measures to assess the information content pertaining to DDS are presented. This approach enables researchers, system architects, system designers, solution providers, and system integrators to be aware of various challenges at the system and subsystem level. The proposed method helps to provide pointers to the literature where information about challenges and specific solutions can be obtained.

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“…Flórez et al 32 . review the state‐of‐the‐art techniques, methods, and algorithms used in video, RF, and audio‐based DDS.…”

Section: The Survey and Measures For Analysismentioning

confidence: 99%

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

Kashi,

Prashanth,

Kashi

et al. 2023

Systems Engineering

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show abstract

“…So far, various modalities, including radar, audio, radio frequency (RF), camera, have been proposed for detection, tracking, classification for eventual neutralization of drones [15]. Multimodal approaches combining two or more of these individual modalities have also been proposed [16].…”

Section: Previous Workmentioning

confidence: 99%

Extracting Unambiguous Drone Signature Using High-Speed Camera

et al. 2022

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In recent years, the use of drones for recreational and commercial activities has grown rapidly due to their affordability and performance. This growing use raises concerns about the threats drones pose to the security of sensitive areas such as airports, prisons, industrial and military facilities. In response to these threats, drones detection methods are being actively developed. In particular, most camera-based methods rely on appearance to perform detection. They are therefore prone to error due to the great similarity between drones and some other flying entities such as birds. However, from a kinematic perspective, unlike birds, drones, especially multicopters, have a propeller rotation speed. The method proposed in this paper uses the propeller rotation speed as the key physical parameter on which to rely to unambiguously distinguish drones from other flying entities. The basic idea consists in using discrete Fourier transform to determine the propellers rotation speed from high frame rate videos, and extracting the propellers induced drone signature as a quantitative camera-based drone signature. The proposed algorithm proceeds as follows: flying entities are continuously tracked in the sky; discrete Fourier transform, applied to the video stream within a time window ending at the current instant (frame), is used to extract the propellers induced drone signature which unambiguously confirm each flying entity as being a drone or not. Experimental results obtained using a consumer-grade camera at a frame rate of 240Hz demonstrate the effectiveness and reliability of the proposed method.INDEX TERMS Drone detection, discrete Fourier transform, high speed camera, propellers induced drone signature, propeller rotation speed, unambiguous drone signature.

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“…Popular methods include radar systems, electro-optical/infrared (EO/IR) imaging, radio frequency (RF) analysis, and sound/noise analysis of UAVs [8]. Radar systems are dominantly used compared to other techniques [8], [25], [26]. However, the probability of miss detection for UAVs using radar systems is high mainly due to the small radar cross section (RCS), RCS similarity to RCS of birds, and high maneuverability close to the terrain.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

Khawaja¹

2022

Preprint

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<p> The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are also provided. </p>

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A review of algorithms, methods, and techniques for detecting UAVs and UAS using audio, radiofrequency, and video applications

Cited by 23 publications

References 48 publications

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

A survey and analysis of drone detection systems using a systems approach superposed on scenarios

Extracting Unambiguous Drone Signature Using High-Speed Camera

A Survey on Radar Techniques for Detection,Tracking, and Classification of Aerial Threats

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