Drone Detection With X-Band Ubiquitous Radar

Quevedo, Álvaro Duque de; Urzaiz, Fernando Ibañez; Menoyo, Javier Gismero; López, A.

doi:10.23919/irs.2018.8447942

Cited by 24 publications

(8 citation statements)

References 6 publications

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“…[64] demonstrated the detection of a small hexacopter using 32 by 8 element L-Band receiver array, which achieved good detection sensitivity against micro-UAS. Similar research was presented in [65], a ubiquitous FMCW radar system working at 8.75 GHz (X-band) with PC-based signal processor is presented. The results indicated that it has the capability to detect a micro-UAS at a range of 2 km with an excellent range-speed association.…”

Section: Radar Based Uas Detectionmentioning

confidence: 68%

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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Section: Radar Based Uas Detectionmentioning

confidence: 68%

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

Wang,

Liu,

Song

2020

Preprint

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“…In this case, a machine learning decision tree classifier is used to reject small objects while maintaining a high probability of detection for the drone. A similar study is presented in [ 8 ], where a ubiquitous frequency modulated continuous wave (FMCW) radar system working at 8.75 GHz (X-band) with PC-based signal processor can detect a micro-UAV at a range of 2 km with an excellent range–speed resolution.…”

Section: Review Of the State-of-the-art Technologymentioning

confidence: 75%

“…Other approaches use multiple antennas following a MIMO approach. The advantage of this approach is in its applicability to a radar system with lower carrier frequencies, as in [ 7 , 8 ]. A Holographic RadarTM (HR) with a 2D antenna array and an appropriate signal processing is used in [ 7 ].…”

Section: Review Of the State-of-the-art Technologymentioning

confidence: 99%

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Besada

Campaña

Carramiñana

et al. 2021

Sensors

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Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.

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“…According to the studies, the dataset for drone detection is obtained using active and passive sensors [35,36]. In studies related to the detection and recognition of drones using active sensors, the use of radar and LIDAR sensors is discussed [14,35,37]. Problems with both of these sensors include high costs and limited integration into small drones.…”

Section: Related Workmentioning

confidence: 99%

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

et al. 2022

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Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

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Drone Detection With X-Band Ubiquitous Radar

Cited by 24 publications

References 6 publications

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

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

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

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