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Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the m...
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... has been much less explored compared to the classification phase where, in most cases, the presence of a target is assumed upfront. A comparison among the different technologies adopted in the literature is summarized in Table 1, with specific focus on the type of approaches used for identifying the presence of drones as well as on the main pros and cons of each technology. Very effective for drone detection 3. ...Similar publications
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This paper presents a novel two-view Structure-from-Motion (SfM) algorithm with the application of multiple Feature Detector Operators (FDO). The key of this study is the implementation of multiple FDOs into a two-view SfM algorithm. The two-view SfM algorithm workflow can be divided into three general steps: feature detection and matching, pose es...
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... In general, optical methods are more suitable for the detection and classification of small UAVs at short ranges, but they suffer from low or variable lights and also obstacles effects. Also, sound detection methods used for the direction of arrival-based identification are effectively appropriate for short distances, and distinguishing small UAVs like quadcopters from birds [8]. ...
... The Laser Imaging Detection and Ranging (LIDAR) technique, which operates by emitting laser pulses, is very sensitive to weather conditions, smoke, and direct sunlight [8]. In contrast, radar-based methods are more effective for detecting quadcopters [8], as they are immune to light and adverse weather conditions. ...
... The Laser Imaging Detection and Ranging (LIDAR) technique, which operates by emitting laser pulses, is very sensitive to weather conditions, smoke, and direct sunlight [8]. In contrast, radar-based methods are more effective for detecting quadcopters [8], as they are immune to light and adverse weather conditions. For example, in [10], an Frequency Modulation Continuous Wave (FMCW) method is used to detect the micro-Doppler pattern caused by the UAV rotor. ...
The effect of noise on the Inverse Synthetic Aperture Radar (ISAR) with sparse apertures is a challenging issue for image reconstruction with high resolution at low Signal-to-Noise Ratios (SNRs). It is well-known that the image resolution is affected by the bandwidth of the transmitted signal and the Coherent Processing Interval (CPI) in two dimensions, range and azimuth, respectively. To reduce the noise effect and thus increase the two-dimensional resolution of Unmanned Aerial Vehicles (UAVs) images, we propose the Fast Reweighted Atomic Norm Denoising (FRAND) algorithm by incorporating the weighted atomic norm minimization. To solve the problem, the Two-Dimensional Alternating Direction Method of Multipliers (2D-ADMM) algorithm is developed to speed up the implementation procedure. Assuming sparse apertures for ISAR images of UAVs, we compare the proposed method with the MUltiple SIgnal Classification (MUSIC), Cadzow, and SL0 methods in different SNRs. Simulation results show the superiority of FRAND at low SNRs based on the Mean-Square Error (MSE), Peak Signal-to-Noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) criteria.
... Therefore, it is necessary to detect illegal UAVs in many different conditions and with diverse sizes. The detection of flying-object can be divided into detection methods based on Radar [1], radio frequency waves [2], or image and video [3]. In [4,5], authors indicate that the image-based detection methods are less sensitive to environmental noise compared with other methods. ...
Flying-object detection has become an increasingly attractive avenue for research, particularly with the rising prevalence of unmanned aerial vehicle (UAV). Utilizing deep learning methods offers an effective means of detection with high accuracy. Meanwhile, the demand to implement deep learning models on embedded devices is growing, fueled by the requirement for capabilities that are both real-time and power efficient. FPGA have emerged as the optimal choice for its parallelism, flexibility and energy efficiency. In this paper, we propose an FPGA-based design for YOLOv4 network to address the problem of flying-object detection. Our proposed design explores and provides a suitable solution for overcoming the challenge of limited floating-point resources while maintaining the accuracy and obtain real-time performance and energy efficiency. We have generated an appropriate dataset of flying objects for implementing, training and fine-tuning the network parameters base on this dataset, and then changing some suitable components in the YOLO networks to fit for the deployment on FPGA. Our experiments in Xilinx ZCU104 development kit show that with our implementation, the accuracy is competitive with the original model running on CPU and GPU despite the process of format conversion and model quantization. In terms of speed, the FPGA implementation with the ZCU104 kit is inferior to the ultra high-end GPU, the RTX 2080Ti, but outperforms the GTX 1650. In terms of power consumption, the FPGA implementation is significantly lower than the GPU GTX 1650 about 3 times and about 7 times lower than RTX 2080Ti. In terms of energy efficiency, FPGA is completely superior to GPU with 2–3 times more efficient than the RTX 2080Ti and 3–4 times that of the GTX 1650.
... A potential hurdle for radar-based detection is that the small RCS of UAVs contributes to weak radar returns, often buried in noise and clutter, making them difficult to distinguish from the background [8]. To address this challenge, NATO has categorized UAVs based on size and RCS, among other parameters, which are crucial for defining radar-based detection capabilities. ...
Unmanned aerial vehicles (UAVs) are increasing in popularity in various sectors, simultaneously rasing the challenge of detecting those with low radar cross sections (RCS). This review paper aims to assess the current state-of-the-art in radar technology, focusing on multiple-input multiple-output (MIMO) and beamforming techniques, to address this growing concern. It explores the challenges associated with detecting UAVs in urban settings and adverse weather conditions, where traditional radar systems often do not succeed. This paper examines the existing literature and technological advancements to understand how these methodologies can significantly boost detection capabilities under the constraints of low RCS. In particular, MIMO technology, renowned for its spatial multiplexing, and beamforming, with its directional signal enhancement, are evaluated for their efficacy in the context of UAV surveillance and defense strategies. Ultimately, a comprehensive comparison is presented, drawing on a variety of studies to illustrate the combined potential of integrating these technologies, providing the way for future developments in radar system design and UAV detection.
... [27] Survey on DL-based UAV detection, with a focus on radar technologies up to the year 2021. [28] Survey on ML-based radar sensor networks for detecting and classifying multirotor UAVs up to the year 2020. [29] Survey on the detection of unauthorized UAVs up to the year 2022. ...
Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.
... Therefore, antidrone systems must be able to detect and distinguish the threat drones. Radar is attracting significant attentions as an effective drone detection technology because they are not affected by weather conditions [2]. ...
In this paper, we propose an inverse synthetic aperture radar (ISAR) imaging method using a millimeter wave (mmW) fast chirp modulation (FCM) multi-input and multi-output (MIMO) radar for drone detection. To obtain high-resolution ISAR images, the backprojection algorithm is adopted for the ISAR processing. This algorithm is suitable for ISAR imaging of a moving drone with an irregular motion. The measurement experiments were conducted using flying drones (DJI Phantom 3, DJI Mavic Pro and DJI Mavic Mini) in indoor situations to examine the effectiveness of our proposal. The measurement results demonstrated that the proposed method could generate the high-resolution ISAR images enough to recognize the approximate size and shape of each drone.
... Modern Counter-UAV systems build upon a number of detection technologies (e.g. visual, RF, radar, acoustic) [9], [10], to overcome specific challenges and limitations of each individual modality. ...
This paper presents the 6th edition of the Drone-vs-Bird detection challenge, jointly organized with the WOSDETC workshop within the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023. The main objective of the challenge is to advance the current state-of-the-art in detecting the presence of one or more Unmanned Aerial Vehicles (UAVs) in real video scenes, while facing challenging conditions such as moving cameras, disturbing environmental factors, and the presence of birds flying in the foreground. For this purpose, a video dataset was provided for training the proposed solutions, and a separate test dataset was released a few days before the challenge deadline to assess their performance. The dataset has continually expanded over consecutive installments of the Drone-vs-Bird challenge and remains openly available to the research community, for non-commercial purposes. The challenge attracted novel signal processing solutions, mainly based on deep learning algorithms. The paper illustrates the results achieved by the teams that successfully participated in the 2023 challenge, offering a concise overview of the state-of-the-art in the field of drone detection using video signal processing. Additionally, the paper provides valuable insights into potential directions for future research, building upon the main pros and limitations of the solutions presented by the participating teams.
... In recent years, many research works have been published to address UAV detection, tracking, and classification problems. The main drone detection technologies are: radar sensors [24][25][26][27][28][29][30][31][32][33][34][35][36], RF sensors [37][38][39][40][41][42][43][44][45][46][47], audio sensors [48][49][50][51][52][53][54][55][56][57][58][59][60], and camera sensors using visual UAV characteristics [61][62][63][64][65][66][67][68][69][70][71]. Based on the above-mentioned sources, the advantages and disadvantages of each drone detection technology are compared in Table 2. ...
... (5) Frequency-modulated continuous wave (FMCW) radar: FMCW radar continually transmits an electromagnetic signal with a fluctuating frequency over time and uses the difference in frequencies between emitted and reflected signals to determine the range and velocity of objects [17]. These signals, often known as chirps, vary from CW in that the operational frequency is not changed throughout the transmission [29]. Due to their constant pulsing, inexpensive cost of hardware components, and superior performance, FMCW and CW radars are recommended for use in UAV detection and identification [30]. ...
The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones’ dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.
... A review of networked radar is presented in ref. [29] which gives an overview of how synchronized networks of sensors are used in various applications, but then focuses more on the state of the art data fusion techniques. Another full review of network radars focused on detection of S-UAS targets is found in ref. [30]. Various networked radars that are developed for the purpose of drone detection are described. ...
The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar.
... Due to the narrow radar cross section of a mini-drone, conventional radar systems will fail to detect it (RCS). To address this issue, researchers used a multistatic radar [4] or a Frequency Modulated Continuous Wave (FMCW) radar [5], [6] to identify and categorise a Quadcopter or Multi-rotor UAV's micro-Doppler signature. [6] provides a comprehensive analysis of the detection and classification capabilities of modern SoA FMCW radars. ...
... To address this issue, researchers used a multistatic radar [4] or a Frequency Modulated Continuous Wave (FMCW) radar [5], [6] to identify and categorise a Quadcopter or Multi-rotor UAV's micro-Doppler signature. [6] provides a comprehensive analysis of the detection and classification capabilities of modern SoA FMCW radars. Researchers presented numerous drone detection techniques employing this technology in [7]- [9]. ...
Despite various positive uses, drones are also utilised for illegal purposes like as drug trafficking, weapon smuggling, and posing dangers to security-sensitive locations such as airports and nuclear power plants. Existing drone localisation and neutralisation solutions are predicated on the drone having previously been discovered and categorised. Despite significant progress in the sensor sector over the last decade, no viable drone detection and classification approach has been suggested in the literature. The frequency signature of the sent signal is used in this research to identify and classify drones using radio frequency (RF). Using commercial drones, we built an unique drone RF dataset and provided a comprehensive comparison of a two-stage and integrated detection and classification framework. Both frameworks' detection and classification results are demonstrated for a single-signal and simultaneous multi-signal situation. We demonstrate that the You Only Look Once (YOLO) framework outperforms the Goodness-of-Fit (GoF) spectrum sensing framework in a simultaneous multi-signal situation, and that it outperforms the Deep Residual Neural Network (DRNN) framework in classification
... For the past half century, multirotor unmanned aerial vehicles (UAVs) have developed rapidly and have been successfully applied in the fields of aerial photography, industrial inspection, and precision agriculture [1][2][3]. However, most of the current applications are based on small-sized multirotors, which have limited available space and load capacity. ...
Advancements in aerial mobility (AAM) are driven by needs in transportation, logistics, rescue, and disaster relief. Consequently, large-sized multirotor unmanned aerial vehicles (UAVs) with strong power and ample space show great potential. In order to optimize the design process for large-sized multirotors and reduce physical trial and error, a detailed dynamic model is firstly established, with an accurate aerodynamic model. In addition, the center of gravity (CoG) offset and actuator dynamics are also well considered, which are usually ignored in small-sized multirotors. To improve the endurance and maneuverability of large-sized multirotors, which is the key concern in real applications, a two-loop optimization method for rotor tilt angle design is proposed based on the mathematical model established previously. Its inner loop solves the dynamic equilibrium points to relax the complex dynamic constraints caused by aerodynamics in the overall optimization problem, which improves the solution efficiency. The ideal design results can be obtained through the offline process, which greatly reduces the difficulties of physical trial and error. Finally, various experiments are carried out to demonstrate the accuracy of the established model and the effectiveness of the optimization method.
