Deep learning–based fifth-generation millimeter-wave communication channel tracking for unmanned aerial vehicle Internet of things networks

Using unmanned aerial vehicle as movable base stations is a promising approach to enhance network coverage. Moreover, movable unmanned aerial vehicle–base stations can dynamically move to the target devices to expand the communication range as relays in the scenario of the Internet of things. In this article, we consider a communication system with movable unmanned aerial vehicle–base stations in millimeter-Wave. The movable unmanned aerial vehicle–base stations are equipped with antennas and multiple sensors for channel tracking. The cylindrical array antenna is mounted on the movable unmanned aerial vehicle–movable base stations, making the beam omnidirectional. Furthermore, the attitude estimation method using the deep neural network can replace the traditional attitude estimation method. The estimated unmanned aerial vehicle attitude information is combined with beamforming technology to realize a reliable communication link. Simulation experiments have been performed, and the results have verified the effectiveness of the proposed method.

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Detection and identification technology of rotor unmanned aerial vehicles in 5G scene

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719853990 ◽

2019 ◽

Vol 15 (6) ◽

pp. 155014771985399 ◽

Cited By ~ 3

Author(s):

Fengtong Xu ◽

Tao Hong ◽

Jingcheng Zhao ◽

Tao Yang

Keyword(s):

Internet Of Things ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Millimeter Wave ◽

Millimeter Wave Radar ◽

Aerial Vehicles ◽

Wave Radar ◽

Radar Echo ◽

Aerial Vehicle ◽

Modulation Signal

In the 5G era, integration between different networks is required to realize a new world of Internet of things, the most typical model is Space–Air–Ground Internet of things. In the Space–Air–Ground Internet of things, unmanned aerial vehicle network is widely used as the representative of air-based networks. Therefore, a lot of unmanned aerial vehicle “black flying” incidents have occurred. UAVs are a kind of “low, slow and small” artificial targets, which face enormous challenges in detecting, identifying, and managing them. In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. For a one-rotor unmanned aerial vehicle, the radar echo data are a single-component sinusoidal frequency modulation signal. The echo signal is conjugated first and then is subjected to a short-time Fourier transform, while the micro-Doppler has a double effect. For a multi-rotor unmanned aerial vehicle, the radar echo data are a multi-component sinusoidal frequency modulation signal, the k-order Bessel function base and the signal are used for integral projection processing, which better identifies the micro-Doppler characteristics such as the number of rotors or the rotational speed of each rotor. The noise interference is added to verify that the algorithm has better robustness. The micro-Doppler characteristics of rotor unmanned aerial vehicles are extracted by the above algorithm, and the data sets are built to train the model. Finally, the classification of unmanned aerial vehicle is realized, and the classification results are given. The research in this article provides an effective solution to solve the problem of detecting and identifying unmanned aerial vehicle by 5G millimeter wave radar in the Internet of Things, which has high practical application value.

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Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

2018 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2018.8489118 ◽

2018 ◽

Cited By ~ 3

Author(s):

Yaohua Liu ◽

Yimin Zhou ◽

Xiang Li

Keyword(s):

Neural Network ◽

Unmanned Aerial Vehicle ◽

Attitude Estimation ◽

Aerial Vehicle

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Facility Location for Unmanned Aerial Vehicle Base Stations to Provide Uninterrupted Mobile Communication After Earthquakes

Lecture Notes in Mechanical Engineering - Digital Conversion on the Way to Industry 4.0 ◽

10.1007/978-3-030-62784-3_5 ◽

2020 ◽

pp. 53-61

Author(s):

Gamze Sevdik ◽

Sakir Esnaf ◽

Engin Baytürk

Keyword(s):

Facility Location ◽

Unmanned Aerial Vehicle ◽

Mobile Communication ◽

Base Stations ◽

Aerial Vehicle

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Unmanned Aerial Vehicle Applications for Military GIS Task Solutions

Automated Systems in the Aviation and Aerospace Industries - Advances in Mechatronics and Mechanical Engineering ◽

10.4018/978-1-5225-7709-6.ch010 ◽

2019 ◽

pp. 273-296 ◽

Cited By ~ 1

Author(s):

Azad Agalar Bayramov ◽

Elshan Giyas Hashimov ◽

Yashar Ali Nasibov

Keyword(s):

Unmanned Aerial Vehicle ◽

Quantitative Method ◽

Estimation Method ◽

Mountainous Terrain ◽

Numerical Estimation ◽

Gis Technology ◽

Geoinformation System ◽

Task Support ◽

Mountain Terrain ◽

Aerial Vehicle

In the chapter, the authors present the results of unmanned aerial vehicle (UAV) applications for military geoinformation system (GIS) task solutions. The results of the visual modelling of the revealing process of invisible military objects from one of point of observation on the mountainous terrain of the Azerbaijan Republic by using UAV are presented. The observation conditions between two points of the selected mountain terrain during battle operation have been investigated using GIS technology. The quantitative method of the invisible area assessment and military objects in mountainous terrain are developed and offered by using UAVs. The numerical estimation method of a task support success of UAV reconnaissance flight in mountainous battle conditions has been offered and considered. Using UAVs for the purpose of orthophotomap making of the terrain and combat control the detailed 3D-model has been constructed.

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Unmanned Aerial Vehicle Applications for Military GIS Task Solutions

Research Anthology on Reliability and Safety in Aviation Systems, Spacecraft, and Air Transport ◽

10.4018/978-1-7998-5357-2.ch044 ◽

2021 ◽

pp. 1092-1115

Author(s):

Azad Agalar Bayramov ◽

Elshan Giyas Hashimov ◽

Yashar Ali Nasibov

Keyword(s):

Unmanned Aerial Vehicle ◽

Quantitative Method ◽

Estimation Method ◽

Mountainous Terrain ◽

Numerical Estimation ◽

Gis Technology ◽

Geoinformation System ◽

Task Support ◽

Mountain Terrain ◽

Aerial Vehicle

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Performance Analysis in Object Diameter Estimation based on Single and Multiple Non-Destructive and Discrete Infrared Sensors

Jurnal Teknologi ◽

10.11113/jt.v67.2028 ◽

2014 ◽

Vol 67 (1) ◽

Author(s):

Norashikin M. Thamrin ◽

Norhashim Mohd. Arshad ◽

Ramli Adnan ◽

Rosidah Sam ◽

Noorfazdli Abd. Razak ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Laser Scanning ◽

Pass Rate ◽

Small Scale ◽

Infrared Sensors ◽

Scanning Method ◽

Average Percentage ◽

Multiple Sensors ◽

Localization And Mapping ◽

Aerial Vehicle

In Simultaneous Localization and Mapping (SLAM) technique, recognizing and marking the landmarks in the environment is very important. Therefore, in a commercial farm, rows of trees, borderline of rows as well as the trees and other features are mostly used by the researchers in realizing the automation process in this field. In this paper, the detection of the tree based on its diameter is focused. There are few techniques available in determining the size of the tree trunk inclusive of the laser scanning method as well as image-based measurements. However, those techniques require heavy computations and equipments which become constraints in a lightweight unmanned aerial vehicle implementation. Therefore, in this paper, the detection of an object by using a single and multiple infrared sensors on a non-stationary automated vehicle platform is discussed. The experiments were executed on different size of objects in order to investigate the effectiveness of this proposed method. This work is initially tested on the ground, based in the lab environment by using an omni directional vehicle which later will be adapted on a small-scale unmanned aerial vehicle implementation for tree diameter estimation in the agriculture farm. In the current study, comparing multiple sensors with single sensor orientation showed that the average percentage of the pass rate in the pole recognition for the former is relatively more accurate than the latter with 93.2 percent and 74.2 percent, respectively.

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