• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.1
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• pp.74-81
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• 2021

UAM(Urban Air Mobility) systems have evolved in the form of helicopters in the 1960~1970s, tiltrotors in the 1980s, small aircraft transportation systems in the 2000s, and electric-powered Vertical Take-Off and Landing (eVTOL) in the 2010s; accordingly, the early heliport has evolved to its current form of a Vertiport. Vertical Takeoff and Landing Sites, Vertiports, are important factors for the successful introduction of UAM, along with the resolution of air traffic control (ATC), air security, and noise problems. However, there are no domestic or international installation standards and guidelines yet. Therefore, in this study, installation standards were prepared by referring to domestic and international case studies, ICAO standards, and MIT research papers. The study proposes to establish standards for Final Approach and Takeoff Area (FATO) as 1.5D, 1D for Touchdown and Lift-Off Area (TLOF), and 1.5D for Safety Area (SA). It also proposes to add "UAM Vertiport Installation Standards" to the 「Act on the Promotion and Foundation of Drone Utilization, Drone Act」.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.903-921
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• 2024

Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.144-153
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• 2017

In recent years, unmanned aerial vehicles (UAVs) have been developed and studied for various applications, including drone deliveries, broadcasting, scouting, crop dusting, and firefighting. To enable the wide use of UAVs, their exact aeroacoustic characteristics must be assessed. In this study, a noise prediction method for a ducted fan UAV with complicated geometry was developed. In general, calculation efficiency is increased by simulating a ducted fan UAV without the struts that fix the fuselage to the ducts. However, numerical predictions of noise and aerodynamics differ according to whether struts are present. In terms of aerodynamic performance, the total thrust with and without struts is similar owing to the tendency of the thrust of a blade to offset the drag of the struts. However, in aeroacoustic simulations, the strut effect should be considered in order to predict the UAV's noise because noise from the blades can be changed by the strut effect. Modelling of the strut effect revealed that the dominant tonal noises were closely correlated with the blade passage frequency of the experimental results. Based on the successful detection of noise sources from a ducted fan UAV system, using the proposed noise contribution contour, methods for noise reduction can be suggested by comparing numerical results with measured noise profiles.

• ETRI Journal
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• v.40 no.2
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• pp.188-196
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• 2018

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

• Convergence Security Journal
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• v.23 no.3
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• pp.73-81
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• 2023

The progress in science and technology has widened the scope of the battlefield, leading to the emergence of cyber electronic warfare that exploits electromagnetic waves and networks. Drones have become more important due to advancements in battery technology and navigation systems. Nevertheless, tackling drone threats comes with its own set of difficulties. Radar plays a vital role in detecting drones, offering long-range capabilities and independence from weather conditions. However, the battlefield presents unique challenges like dealing with high levels of signal noise and ensuring the safety of the detection assets. This paper proposes various approaches to improve the operation of drone detection radar in cyber electronic warfare, with a focus on enhancing signal processing techniques, utilizing low probability of interception (LPI) radar, and implementing optimized deployment strategies.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.12-25
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• 2020

The purpose of this study was to obtain information about the type of drones suitable for the management of entrants and entrants of islands in the marine national park. The research sites were 25 islands in the Hallyeohaesang National Park. The target islands were divided into three zones, and were investigated with different types of drones. The survey period was from October to November, 2019. As a result of the operation of drone airframe, drone with fixed wings was found to be favorable for the management of marine parks in medium and long distances compared to other types, but stopping flights for broadcasting was found to be unsuitable. Drone with rotational wings was found to be suitable for image acquisition and broadcasting through close flight. However, it was deemed suitable for short and medium distance flights because of the fast battery consumption. In the case of helicopter rotorcraft drone, image acquisition and broadcasting were possible, but noise and vibration caused by propellers were disadvantageous. The number of entrants to the islands totaled 410 and the main act was fishing. The proportion of entrants to the islands in Area A was higher than that of other areas, and thus it was deemed more necessary to manage the area. Broadcasting was found to have had a positive effect on the management of fishers.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.