Drones

Editorial

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7 pages, 195 KiB

Open AccessEditorial

Editorial of Special Issue “Advances in UAV Detection, Classification and Tracking”

by Daobo Wang and Zain Anwar Ali

Drones 2023, 7(3), 195; https://doi.org/10.3390/drones7030195 - 14 Mar 2023

Viewed by 1795

Abstract

This is an editorial for a Special Issue of Drones titled “Advances in UAV Detection, Classification and Tracking” [...] Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

Research

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19 pages, 24783 KiB

Open AccessArticle

Simultaneous Astronaut Accompanying and Visual Navigation in Semi-Structured and Dynamic Intravehicular Environment

by Qi Zhang, Li Fan and Yulin Zhang

Drones 2022, 6(12), 397; https://doi.org/10.3390/drones6120397 - 6 Dec 2022

Cited by 1 | Viewed by 1442

Abstract

The application of intravehicular robotic assistants (IRA) can save valuable working hours for astronauts in space stations. There are various types of IRA, such as an accompanying drone working in microgravity and a dexterous humanoid robot for collaborative operations. In either case, the [...] Read more.

The application of intravehicular robotic assistants (IRA) can save valuable working hours for astronauts in space stations. There are various types of IRA, such as an accompanying drone working in microgravity and a dexterous humanoid robot for collaborative operations. In either case, the ability to navigate and work along with human astronauts lays the foundation for their deployment. To address this problem, this paper proposes the framework of simultaneous astronaut accompanying and visual navigation. The framework contains a customized astronaut detector, an intravehicular navigation system, and a probabilistic model for astronaut visual tracking and motion prediction. The customized detector is designed to be lightweight and has achieved superior performance ([email protected] of 99.36%) for astronaut detection in diverse postures and orientations during intravehicular activities. A map-based visual navigation method is proposed for accurate and 6DoF localization (1~2 cm, 0.5°) in semi-structured environments. To ensure the robustness of navigation in dynamic scenes, feature points within the detected bounding boxes are filtered out. The probabilistic model is formulated based on the map-based navigation system and the customized astronaut detector. Both trajectory correlation and geometric similarity clues are incorporated into the model for stable visual tracking and trajectory estimation of the astronaut. The overall framework enables the robotic assistant to track and distinguish the served astronaut efficiently during intravehicular activities and to provide foresighted service while in locomotion. The overall performance and superiority of the proposed framework are verified through extensive ground experiments in a space-station mockup. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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19 pages, 13384 KiB

Open AccessArticle

Small-Object Detection for UAV-Based Images Using a Distance Metric Method

by Helu Zhou, Aitong Ma, Yifeng Niu and Zhaowei Ma

Drones 2022, 6(10), 308; https://doi.org/10.3390/drones6100308 - 20 Oct 2022

Cited by 32 | Viewed by 9543

Abstract

Object detection is important in unmanned aerial vehicle (UAV) reconnaissance missions. However, since a UAV flies at a high altitude to gain a large reconnaissance view, the captured objects often have small pixel sizes and their categories have high uncertainty. Given the limited [...] Read more.

Object detection is important in unmanned aerial vehicle (UAV) reconnaissance missions. However, since a UAV flies at a high altitude to gain a large reconnaissance view, the captured objects often have small pixel sizes and their categories have high uncertainty. Given the limited computing capability on UAVs, large detectors based on convolutional neural networks (CNNs) have difficulty obtaining real-time detection performance. To address these problems, we designed a small-object detector for UAV-based images in this paper. We modified the backbone of YOLOv4 according to the characteristics of small-object detection. We improved the performance of small-object positioning by modifying the positioning loss function. Using the distance metric method, the proposed detector can classify trained and untrained objects through object features. Furthermore, we designed two data augmentation strategies to enhance the diversity of the training set. We evaluated our method on a collected small-object dataset; the proposed method obtained 61.00%

m A P_{50}

on trained objects and 41.00%

m A P_{50}

on untrained objects with 77 frames per second (FPS). Flight experiments confirmed the utility of our approach on small UAVs, with satisfying detection performance and real-time inference speed. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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14 pages, 1952 KiB

Open AccessArticle

Elliptical Multi-Orbit Circumnavigation Control of UAVS in Three-Dimensional Space Depending on Angle Information Only

by Zhen Wang and Yanhong Luo

Drones 2022, 6(10), 296; https://doi.org/10.3390/drones6100296 - 10 Oct 2022

Cited by 3 | Viewed by 2185

Abstract

In order to analyze the circumnavigation tracking problem in complex three-dimensional space, in this paper, we propose a UAV group circumnavigation control strategy, in which the UAV circumnavigation orbit is an ellipse whose size can be adjusted arbitrarily; at the same time, the [...] Read more.

In order to analyze the circumnavigation tracking problem in complex three-dimensional space, in this paper, we propose a UAV group circumnavigation control strategy, in which the UAV circumnavigation orbit is an ellipse whose size can be adjusted arbitrarily; at the same time, the UAV group can be assigned to multiple orbits for tracking. The UAVs only have the angle information of the target, and the position information of the target can be obtained by using the angle information and the proposed three-dimensional estimator, thereby establishing an ideal relative velocity equation. By constructing the error dynamic equation between the actual relative velocity and the ideal relative velocity, the circumnavigation problem in three-dimensional space is transformed into a velocity tracking problem. Since the UAVs are easily disturbed by external factors during flight, the sliding mode control is used to improve the robustness of the system. Finally, the effectiveness of the control law and its robustness to unexpected situations are verified by simulation. Full article

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15 pages, 3522 KiB

Open AccessArticle

Detection of Micro-Doppler Signals of Drones Using Radar Systems with Different Radar Dwell Times

by Jiangkun Gong, Jun Yan, Deren Li and Deyong Kong

Drones 2022, 6(9), 262; https://doi.org/10.3390/drones6090262 - 19 Sep 2022

Cited by 13 | Viewed by 9126

Abstract

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the [...] Read more.

Not any radar dwell time of a drone radar is suitable for detecting micro-Doppler (or jet engine modulation, JEM) produced by the rotating blades in radar signals of drones. Theoretically, any X-band drone radar system should detect micro-Doppler of blades because of the micro-Doppler effect and partial resonance effect. Yet, we analyzed radar data detected by three radar systems with different radar dwell times but similar frequency and velocity resolution, including Radar−α, Radar−β, and Radar−γ with radar dwell times of 2.7 ms, 20 ms, and 89 ms, respectively. The results indicate that Radar−β is the best radar for detecting micro-Doppler (i.e., JEM signals) produced by the rotating blades of a quadrotor drone, DJI Phantom 4, because the detection probability of JEM signals is almost 100%, with approximately 2 peaks, whose magnitudes are similar to that of the body Doppler. In contrast, Radar−α can barely detect any micro-Doppler, and Radar−γ detects weak micro-Doppler signals, whose magnitude is only 10% of the body Doppler’s. Proper radar dwell time is the key to micro-Doppler detection. This research provides an idea for designing a cognitive micro-Doppler radar by changing radar dwell time for detecting and tracking micro-Doppler signals of drones. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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16 pages, 7838 KiB

Open AccessArticle

ARSD: An Adaptive Region Selection Object Detection Framework for UAV Images

by Yuzhuang Wan, Yi Zhong, Yan Huang, Yi Han, Yongqiang Cui, Qi Yang, Zhuo Li, Zhenhui Yuan and Qing Li

Drones 2022, 6(9), 228; https://doi.org/10.3390/drones6090228 - 31 Aug 2022

Cited by 6 | Viewed by 2220

Abstract

Due to the rapid development of deep learning, the performance of object detection has greatly improved. However, object detection in high-resolution Unmanned Aerial Vehicles images remains a challenging problem for three main reasons: (1) the objects in aerial images have different scales and [...] Read more.

Due to the rapid development of deep learning, the performance of object detection has greatly improved. However, object detection in high-resolution Unmanned Aerial Vehicles images remains a challenging problem for three main reasons: (1) the objects in aerial images have different scales and are usually small; (2) the images are high-resolution but state-of-the-art object detection networks are of a fixed size; (3) the objects are not evenly distributed in aerial images. To this end, we propose a two-stage Adaptive Region Selection Detection framework in this paper. An Overall Region Detection Network is first applied to coarsely localize the object. A fixed points density-based targets clustering algorithm and an adaptive selection algorithm are then designed to select object-dense sub-regions. The object-dense sub-regions are sent to a Key Regions Detection Network where results are fused with the results at the first stage. Extensive experiments and comprehensive evaluations on the VisDrone2021-DET benchmark datasets demonstrate the effectiveness and adaptiveness of the proposed framework. Experimental results show that the proposed framework outperforms, in terms of mean average precision (mAP), the existing baseline methods by 2.1% without additional time consumption. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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10 pages, 3233 KiB

Open AccessArticle

Using Classify-While-Scan (CWS) Technology to Enhance Unmanned Air Traffic Management (UTM)

by Jiangkun Gong, Deren Li, Jun Yan, Huiping Hu and Deyong Kong

Drones 2022, 6(9), 224; https://doi.org/10.3390/drones6090224 - 27 Aug 2022

Cited by 2 | Viewed by 2362

Abstract

Drone detection radar systems have been verified for supporting unmanned air traffic management (UTM). Here, we propose the concept of classify while scan (CWS) technology to improve the detection performance of drone detection radar systems and then to enhance UTM application. The CWS [...] Read more.

Drone detection radar systems have been verified for supporting unmanned air traffic management (UTM). Here, we propose the concept of classify while scan (CWS) technology to improve the detection performance of drone detection radar systems and then to enhance UTM application. The CWS recognizes the radar data of each radar cell in the radar beam using advanced automatic target recognition (ATR) algorithm and then integrates the recognized results into the tracking unit to obtain the real-time situational awareness results of the whole surveillance area. Real X-band radar data collected in a coastal environment demonstrate significant advancement in a powerful situational awareness scenario in which birds were chasing a ship to feed on fish. CWS technology turns a drone detection radar into a sense-and-alert planform that revolutionizes UTM systems by reducing the Detection Response Time (DRT) in the detection unit. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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21 pages, 6269 KiB

Open AccessFeature PaperArticle

A Modified YOLOv4 Deep Learning Network for Vision-Based UAV Recognition

by Farzaneh Dadrass Javan, Farhad Samadzadegan, Mehrnaz Gholamshahi and Farnaz Ashatari Mahini

Drones 2022, 6(7), 160; https://doi.org/10.3390/drones6070160 - 27 Jun 2022

Cited by 21 | Viewed by 5767

Abstract

The use of drones in various applications has now increased, and their popularity among the general public has increased. As a result, the possibility of their misuse and their unauthorized intrusion into important places such as airports and power plants are increasing, threatening [...] Read more.

The use of drones in various applications has now increased, and their popularity among the general public has increased. As a result, the possibility of their misuse and their unauthorized intrusion into important places such as airports and power plants are increasing, threatening public safety. For this reason, accurate and rapid recognition of their types is very important to prevent their misuse and the security problems caused by unauthorized access to them. Performing this operation in visible images is always associated with challenges, such as the small size of the drone, confusion with birds, the presence of hidden areas, and crowded backgrounds. In this paper, a novel and accurate technique with a change in the YOLOv4 network is presented to recognize four types of drones (multirotors, fixed-wing, helicopters, and VTOLs) and to distinguish them from birds using a set of 26,000 visible images. In this network, more precise and detailed semantic features were extracted by changing the number of convolutional layers. The performance of the basic YOLOv4 network was also evaluated on the same dataset, and the proposed model performed better than the basic network in solving the challenges. Compared to the basic YOLOv4 network, the proposed model provides better performance in solving challenges. Additionally, it can perform automated vision-based recognition with a loss of 0.58 in the training phase and 83% F1-score, 83% accuracy, 83% mean Average Precision (mAP), and 84% Intersection over Union (IoU) in the testing phase. These results represent a slight improvement of 4% in these evaluation criteria over the YOLOv4 basic model. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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14 pages, 1094 KiB

Open AccessEditor’s ChoiceArticle

Anti-Occlusion UAV Tracking Algorithm with a Low-Altitude Complex Background by Integrating Attention Mechanism

by Chuanyun Wang, Zhongrui Shi, Linlin Meng, Jingjing Wang, Tian Wang, Qian Gao and Ershen Wang

Drones 2022, 6(6), 149; https://doi.org/10.3390/drones6060149 - 16 Jun 2022

Cited by 14 | Viewed by 2797

Abstract

In recent years, the increasing number of unmanned aerial vehicles (UAVs) in the low-altitude airspace have not only brought convenience to people’s work and life, but also great threats and challenges. In the process of UAV detection and tracking, there are common problems [...] Read more.

In recent years, the increasing number of unmanned aerial vehicles (UAVs) in the low-altitude airspace have not only brought convenience to people’s work and life, but also great threats and challenges. In the process of UAV detection and tracking, there are common problems such as target deformation, target occlusion, and targets being submerged by complex background clutter. This paper proposes an anti-occlusion UAV tracking algorithm for low-altitude complex backgrounds by integrating an attention mechanism that mainly solves the problems of complex backgrounds and occlusion when tracking UAVs. First, extracted features are enhanced by using the SeNet attention mechanism. Second, the occlusion-sensing module is used to judge whether the target is occluded. If the target is not occluded, tracking continues. Otherwise, the LSTM trajectory prediction network is used to predict the UAV position of subsequent frames by using the UAV flight trajectory before occlusion. This study was verified on the OTB-100, GOT-10k and integrated UAV datasets. The accuracy and success rate of integrated UAV datasets were 79% and 50.5% respectively, which were 10.6% and 4.9% higher than those of the SiamCAM algorithm. Experimental results show that the algorithm could robustly track a small UAV in a low-altitude complex background. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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18 pages, 1786 KiB

Open AccessArticle

Drones Classification by the Use of a Multifunctional Radar and Micro-Doppler Analysis

by Mauro Leonardi, Gianluca Ligresti and Emilio Piracci

Drones 2022, 6(5), 124; https://doi.org/10.3390/drones6050124 - 11 May 2022

Cited by 10 | Viewed by 4539

Abstract

The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. [...] Read more.

The classification of targets by the use of radars has received great interest in recent years, in particular in defence and military applications, in which the development of sensor systems that are able to identify and classify threatening targets is a mandatory requirement. In the specific case of drones, several classification techniques have already been proposed and, up to now, the most effective technique was considered to be micro-Doppler analysis used in conjunction with machine learning tools. The micro-Doppler signatures of targets are usually represented in the form of the spectrogram, that is a time–frequency diagram that is obtained by performing a short-time Fourier transform (STFT) on the radar return signal. Moreover, frequently it is possible to extract useful information that can also be used in the classification task from the spectrogram of a target. The main aim of the paper is comparing different ways to exploit the drone’s micro-Doppler analysis on different stages of a multifunctional radar. Three different classification approaches are compared: classic spectrogram-based classification; spectrum-based classification in which the received signal from the target is picked up after the moving target detector (MTD); and features-based classification, in which the received signal from the target undergoes the detection step after the MTD, after which discriminating features are extracted and used as input to the classifier. To compare the three approaches, a theoretical model for the radar return signal of different types of drone and aerial target is developed, validated by comparison with real recorded data, and used to simulate the targets. Results show that the third approach (features-based) not only has better performance than the others but also is the one that requires less modification and less processing power in a modern multifunctional radar because it reuses most of the processing facility already present. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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17 pages, 38484 KiB

Open AccessArticle

Multi-Target Association for UAVs Based on Triangular Topological Sequence

by Xudong Li, Lizhen Wu, Yifeng Niu and Aitong Ma

Drones 2022, 6(5), 119; https://doi.org/10.3390/drones6050119 - 7 May 2022

Cited by 8 | Viewed by 2603

Abstract

Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is [...] Read more.

Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is difficult for UAVs to correctly distinguish targets that are visually similar to their associations. Incorrect correlation matching between targets will result in incorrect localization and tracking of multiple targets by multiple UAVs. In order to solve the association problem of targets with similar visual characteristics and reduce the localization and tracking errors caused by target association errors, based on the relative positions of the targets, the paper proposes a globally consistent target association algorithm for multiple UAV vision sensors based on triangular topological sequences. In contrast to Siamese neural networks and trajectory correlation, the relative position relationship between targets is used to distinguish and correlate targets with similar visual features and trajectories. The sequence of neighboring triangles of targets is constructed using the relative position relationship, and the feature is a specific triangular network. Moreover, a method for calculating topological sequence similarity with similar transformation invariance is proposed, as well as a two-step optimal association method that considers global objective association consistency. The results of flight experiments indicate that the algorithm achieves an association accuracy of 84.63%, and that two-step association is 12.83% more accurate than single-step association. Through this work, the multi-target association problem with similar or even identical visual characteristics can be solved in the task of cooperative surveillance and tracking of suspicious vehicles on the ground by multiple UAVs. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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15 pages, 13243 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs

by Muhammad Shafiq, Zain Anwar Ali, Amber Israr, Eman H. Alkhammash and Myriam Hadjouni

Drones 2022, 6(5), 104; https://doi.org/10.3390/drones6050104 - 23 Apr 2022

Cited by 11 | Viewed by 3008

Abstract

This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study [...] Read more.

This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimization (MMACO) in conjunction with social learning mechanism to plan the optimized path for an individual colony. Hereinafter, the multi-agent system (MAS) chooses the most optimal UAV as the leader of each colony and the remaining UAVs as agents, which helps to organize the randomly positioned UAVs into three different formations. Afterward, the algorithm synchronizes and connects the three colonies into a swarm and controls it using dynamic leader selection. The major contribution of this study is to hybridize two different approaches to produce a more optimized, efficient, and effective strategy. The results verify that the proposed algorithm completes the given objectives. This study also compares the designed method with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to prove that our method offers better convergence and reaches the target using a shorter route than NSGA-II. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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18 pages, 2952 KiB

Open AccessArticle

Mathematical Modeling and Stability Analysis of Tiltrotor Aircraft

by Hanlin Sheng, Chen Zhang and Yulong Xiang

Drones 2022, 6(4), 92; https://doi.org/10.3390/drones6040092 - 8 Apr 2022

Cited by 16 | Viewed by 5164

Abstract

The key problem in the development process of a tiltrotor is its mathematical modeling. Regarding that, this paper proposes a dividing modeling method which divides a tiltrotor into five parts (rotor, wing, fuselage, horizontal tail, and vertical fin) and to develop aerodynamic models [...] Read more.

The key problem in the development process of a tiltrotor is its mathematical modeling. Regarding that, this paper proposes a dividing modeling method which divides a tiltrotor into five parts (rotor, wing, fuselage, horizontal tail, and vertical fin) and to develop aerodynamic models for each of them. In that way, force and moment generated by each part are obtained. Then by blade element theory, we develop the rotor’s dynamic model and rotor flapping angle expression; by mature lifting line theory, the build dynamic models of the wings, fuselage, horizontal tail and vertical fin and the rotors’ dynamic interference on wings, as well as nacelle tilt’s variation against center of gravity and moment of inertia, are taken into account. In MATLAB/Simulink simulation environment, a non-linear tiltrotor simulation model is built, Trim command is applied to trim the tiltrotor, and the XV-15 tiltrotor is taken as an example to validate rationality of the model developed. In the end, the non-linear simulation model is linearized to obtain a state-space matrix, and thus the stability analysis of the tiltrotor is performed. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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20 pages, 4431 KiB

Open AccessFeature PaperArticle

Design and Implementation of Sensor Platform for UAV-Based Target Tracking and Obstacle Avoidance

by Abera Tullu, Mostafa Hassanalian and Ho-Yon Hwang

Drones 2022, 6(4), 89; https://doi.org/10.3390/drones6040089 - 29 Mar 2022

Cited by 11 | Viewed by 5119

Abstract

Small-scale unmanned aerial vehicles are being deployed in urban areas for missions such as ground target tracking, crime scene monitoring, and traffic management. Aerial vehicles deployed in such cluttered environments are required to have robust autonomous navigation with both target tracking and obstacle [...] Read more.

Small-scale unmanned aerial vehicles are being deployed in urban areas for missions such as ground target tracking, crime scene monitoring, and traffic management. Aerial vehicles deployed in such cluttered environments are required to have robust autonomous navigation with both target tracking and obstacle avoidance capabilities. To this end, this work presents a simple-to-design but effective steerable sensor platform and its implementation techniques for both obstacle avoidance and target tracking. The proposed platform is a 2-axis gimbal system capable of roll and pitch/yaw. The mathematical model that governs the dynamics of this platform is developed. The performance of the platform is validated through a software-in-the-loop simulation. The simulation results show that the platform can be effectively steered to all regions of interest except backward. With its design layout and mount location, the platform can engage sensors for obstacle avoidance and target tracking as per requirements. Moreover, steering the platform in any direction does not induce aerodynamic instability on the unmanned aerial vehicle in mission. Full article

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23 pages, 14194 KiB

Open AccessArticle

Research on Modeling and Fault-Tolerant Control of Distributed Electric Propulsion Aircraft

by Jiacheng Li, Jie Yang and Haibo Zhang

Drones 2022, 6(3), 78; https://doi.org/10.3390/drones6030078 - 17 Mar 2022

Cited by 6 | Viewed by 3315

Abstract

Distributed electric propulsion (DEP) aircrafts have high propulsion efficiency and low fuel consumption, which is very promising for propulsion. The redundant thrusters of DEP aircrafts increase the risk of fault in the propulsion system, so it is necessary to study fault-tolerant control to [...] Read more.

Distributed electric propulsion (DEP) aircrafts have high propulsion efficiency and low fuel consumption, which is very promising for propulsion. The redundant thrusters of DEP aircrafts increase the risk of fault in the propulsion system, so it is necessary to study fault-tolerant control to ensure flight safety. There has been little research on coordinated thrust control, and research on fault-tolerant control of the propulsion system for DEP aircrafts is also in the preliminary stage. In this study, a mathematical model of DEP aircrafts was built. Aiming at the lateral and longitudinal control of DEP aircrafts, a coordinated thrust control method based on total energy control and total heading control was designed. Furthermore, a fault-tolerant control strategy and control method was developed for faults in the propulsion system. Simulation results showed that the controller could control the thrust to the prefault level. The correctness and effectiveness of the designed coordinated thrust control method and the fault-tolerant control method for DEP aircrafts were theoretically verified. This study provides a theoretical basis for future engineering application and development of the control system for DEP aircrafts. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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Review

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20 pages, 3242 KiB

Open AccessReview

Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats

by Ghulam E. Mustafa Abro, Saiful Azrin B. M. Zulkifli, Rana Javed Masood, Vijanth Sagayan Asirvadam and Anis Laouiti

Drones 2022, 6(10), 284; https://doi.org/10.3390/drones6100284 - 1 Oct 2022

Cited by 65 | Viewed by 16063

Abstract

It has been observed that unmanned aerial vehicles (UAVs), also known as drones, have been used in a very different way over time. The advancements in key UAV areas include detection (including radio frequency and radar), classification (including micro, mini, close range, short [...] Read more.

It has been observed that unmanned aerial vehicles (UAVs), also known as drones, have been used in a very different way over time. The advancements in key UAV areas include detection (including radio frequency and radar), classification (including micro, mini, close range, short range, medium range, medium-range endurance, low-altitude deep penetration, low-altitude long endurance, and medium-altitude long endurance), tracking (including lateral tracking, vertical tracking, moving aerial pan with moving target, and moving aerial tilt with moving target), and so forth. Even with all of these improvements and advantages, security and privacy can still be ensured by researching a number of key aspects of an unmanned aerial vehicle, such as through the jamming of the control signals of a UAV and redirecting them for any high-assault activity. This review article will examine the privacy issues related to drone standards and regulations. The manuscript will also provide a comprehensive answer to these limitations. In addition to updated information on current legislation and the many classes that can be used to establish communication between a ground control room and an unmanned aerial vehicle, this article provides a basic overview of unmanned aerial vehicles. After reading this review, readers will understand the shortcomings, the most recent advancements, and the strategies for addressing security issues, assaults, and limitations. The open research areas described in this manuscript can be utilized to create novel methods for strengthening the security and privacy of an unmanned aerial vehicle. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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22 pages, 1517 KiB

Open AccessReview

Optimization Methods Applied to Motion Planning of Unmanned Aerial Vehicles: A Review

by Amber Israr, Zain Anwar Ali, Eman H. Alkhammash and Jari Juhani Jussila

Drones 2022, 6(5), 126; https://doi.org/10.3390/drones6050126 - 13 May 2022

Cited by 35 | Viewed by 7406

Abstract

A system that can fly off and touches down to execute particular tasks is a flying robot. Nowadays, these flying robots are capable of flying without human control and make decisions according to the situation with the help of onboard sensors and controllers. [...] Read more.

A system that can fly off and touches down to execute particular tasks is a flying robot. Nowadays, these flying robots are capable of flying without human control and make decisions according to the situation with the help of onboard sensors and controllers. Among flying robots, Unmanned Aerial Vehicles (UAVs) are highly attractive and applicable for military and civilian purposes. These applications require motion planning of UAVs along with collision avoidance protocols to get better robustness and a faster convergence rate to meet the target. Further, the optimization algorithm improves the performance of the system and minimizes the convergence error. In this survey, diverse scholarly articles were gathered to highlight the motion planning for UAVs that use bio-inspired algorithms. This study will assist researchers in understanding the latest work done in the motion planning of UAVs through various optimization techniques. Moreover, this review presents the contributions and limitations of every article to show the effectiveness of the proposed work. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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Other

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11 pages, 5838 KiB

Open AccessTechnical Note

The Development of a Visual Tracking System for a Drone to Follow an Omnidirectional Mobile Robot

by Jie-Tong Zou and Xiang-Yin Dai

Drones 2022, 6(5), 113; https://doi.org/10.3390/drones6050113 - 29 Apr 2022

Cited by 10 | Viewed by 4035

Abstract

This research aims to develop a visual tracking system for a UAV which guides a drone to track a mobile robot and accurately land on it when it stops moving. Two LEDs with different colors were installed on the bottom of the drone. [...] Read more.

This research aims to develop a visual tracking system for a UAV which guides a drone to track a mobile robot and accurately land on it when it stops moving. Two LEDs with different colors were installed on the bottom of the drone. The visual tracking system on the mobile robot can detect the heading angle and the distance between the drone and mobile robot. The heading angle and flight velocity in the pitch and roll direction of the drone were modified by PID control, so that the flying speed and angle are more accurate, and the drone can land quickly. The PID tuning parameters were also adjusted according to the height of the drone. The embedded system on the mobile robot, which is equipped with Linux Ubuntu and processes images with OpenCV, can send the control command (SDK 2.0) to the Tello EDU drone through WIFI with UDP Protocol. The drone can auto-track the mobile robot. After the mobile robot stops, the drone can land on the top of the mobile robot. From the experimental results, the drone can take off from the top of the mobile robot, visually track the mobile robot, and finally land on the top of the mobile robot accurately. Full article

(This article belongs to the Special Issue Advances in UAV Detection, Classification and Tracking)

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Advances in UAV Detection, Classification and Tracking

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