28 GHz 5G-Based Phased-Arrays for UAV Detection and Automotive Traffic-Monitoring Radars

Wang, Yao-Chen; Phelps, Thomas; Kibaroglu, Kerim; Sayginer, Mustafa; Ma, Qian; Rebeiz, Gabriel M.

doi:10.1109/mwsym.2018.8439272

Cited by 35 publications

(15 citation statements)

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“…5G mmWave technology and distributed networks will facilitate radar detection and identification of small UAVs. The narrow steerable beam enables the base station to function as a millimeter wave radar system so that the base station is able to detect and identify drones in urban environments [12,13]. In addition, through end-to-end fragmentation technology, the 5G network provides a logically isolated virtual private segmentation network for different services to implement a manual flight-specific traffic channel network [14].…”

Section: System Modelmentioning

confidence: 99%

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Zhao

Yang

et al. 2019

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs) have broad application potential for the Internet of Things (IoT) due to their small size, low cost, and flexible control. At present, the main positioning method for UAVs is the use of GPS. However, GPS positioning may be affected by stronger electromagnetic signals from spoofing attacks. In this study, a radar-assisted positioning method based on 5G millimeter waves is proposed. In 5G end-to-end network slices, the rotors of UAVs can be detected and identified by deploying 5G millimeter wave radar. High-resolution range profile (HRRP) is used to obtain the UAV location in the detection zone. Micro-Doppler characteristics are used to identify the UAVs and the cepstrum method is used to extract the number and speed information of the UAV rotor. The sinusoidal frequency modulation (SFM) parameter optimization method is used to separate multiple UAVs. The proposed method provides information on the number of UAVs, the position of the UAV, the number of rotors, and the rotation speed of each rotor. The simulation results show that the proposed radar detection method is well suited for UAV detection and identification and provides a valid GPS-independent method for UAV tracking.

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Section: System Modelmentioning

confidence: 99%

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Zhao

Yang

et al. 2019

Wireless Communications and Mobile Computing

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show abstract

“…Unmanned aerial vehicles (UAVs) 1 autonomously fly using onboard cameras and perform monitoring functions such as traffic regulation using onboard cameras. [2][3][4][5] A pilot can control each UAV by watching UAV camera screens and considering UAV positions. 6,7 However, it is difficult for a single pilot to control UAVs concurrently.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder-based candidate waypoint generation method for autonomous flight of multi-unmanned aerial vehicles

Kwak

Sung

2019

Advances in Mechanical Engineering

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Unmanned aerial vehicles may collide with obstacles, such as trees or other unmanned aerial vehicles, while flying. A waypoint-based flight path is an approach to avoid such obstacles. To specify waypoints for the safe flight of unmanned aerial vehicles, it is necessary to define a flight path in advance by analyzing the flight records of unmanned aerial vehicles and thereby designate the waypoints automatically. However, there is a problem in that pilots tend to make errors in controlling unmanned aerial vehicles and collecting flight records. This article proposes a method to generate candidate waypoints for a flight path by removing such unintended flight records. In this method, images representing the positions in the collected flight records are generated. The candidate waypoints are generated as positions corresponding to the overlapping pixels of the images generated via image accumulation based on the flight records and the ones generated by accumulating the images reconstructed using an Autoencoder. The unmanned aerial vehicles can be set the waypoints for an autonomous flight using the candidate waypoints. An experiment was conducted in a university to generate candidate waypoints for road monitoring. The results obtained using the proposed method and K-means algorithm were compared. The candidate waypoints generated using the proposed method were reduced by 84.21% compared to those generated using the K-means algorithm.

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“…Micro unmanned aircraft systems (micro UAS) [1] equipped with a camera [2][3][4] have been used in applications such as traffic surveillance and hobby filming. In addition, data collected from the micro UAS is used to perform real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, precision agriculture, and civil infrastructure inspection for various civil applications [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

End-To-End Controls Using K-Means Algorithm for 360-Degree Video Control Method on Omnidirectional Camera-Equipped Autonomous Micro Unmanned Aircraft Systems

Kwak¹,

Sung²

2019

Applied Sciences

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Micro unmanned aircraft systems (micro UAS)-related technical research is important because micro UAS has the advantage of being able to perform missions remotely. When an omnidirectional camera is mounted, it captures all surrounding areas of the micro UAS. Normal field of view (NFoV) refers to a view presented as an image to a user in a 360-degree video. The 360-degree video is controlled using an end-to-end controls method to automatically provide the user with NFoVs without the user controlling the 360-degree video. When using the end-to-end controls method that controls 360-degree video, if there are various signals that control the 360-degree video, the training of the deep learning model requires a considerable amount of training data. Therefore, there is a need for a method of autonomously determining the signals to reduce the number of signals for controlling the 360-degree video. This paper proposes a method to autonomously determine the output to be used for end-to-end control-based deep learning model to control 360-degree video for micro UAS controllers. The output of the deep learning model to control 360-degree video is automatically determined using the K-means algorithm. Using a trained deep learning model, the user is presented with NFoVs in a 360-degree video. The proposed method was experimentally verified by providing NFoVs wherein the signals that control the 360-degree video were set by the proposed method and by user definition. The results of training the convolution neural network (CNN) model using the signals to provide NFoVs were compared, and the proposed method provided NFoVs similar to NFoVs of existing user with 24.4% more similarity compared to a user-defined approach.

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28 GHz 5G-Based Phased-Arrays for UAV Detection and Automotive Traffic-Monitoring Radars

Cited by 35 publications

References 1 publication

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Autoencoder-based candidate waypoint generation method for autonomous flight of multi-unmanned aerial vehicles

End-To-End Controls Using K-Means Algorithm for 360-Degree Video Control Method on Omnidirectional Camera-Equipped Autonomous Micro Unmanned Aircraft Systems

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