Review of radar classification and RCS characterisation techniques for small UAVs or drones

This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration are briefly outlined. Key difficulties involved in the identification and hence tracking of these `radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and radar cross section (RCS) characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture (precision agriculture, facility agriculture, and order agriculture) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures (authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies (5 G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture) are described as the future research directions of smart agriculture

A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques

Unmanned Aerial Vehicles (UAV) have revolutionized the aircraft industry in this decade. UAVs are now capable of carrying out remote sensing, remote monitoring, courier delivery, and a lot more. A lot of research is happening on making UAVs more robust using energy harvesting techniques to have a better battery lifetime, network performance and to secure against attackers. UAV networks are many times used for unmanned missions. There have been many attacks on civilian, military, and industrial targets that were carried out using remotely controlled or automated UAVs. This continued misuse has led to research in preventing unauthorized UAVs from causing damage to life and property. In this paper, we present a literature review of UAVs, UAV attacks, and their prevention using anti-UAV techniques. We first discuss the different types of UAVs, the regulatory laws for UAV activities, their use cases, recreational, and military UAV incidents. After understanding their operation, various techniques for monitoring and preventing UAV attacks are described along with case studies

UAV Cloud Platform for Precision Farming

A new application for Unmanned Aerial Vehicles comes to light daily to solve some of modern society’s problems. One of the mentioned predicaments is the possibility for optimization in agricultural processes. Due to this, a new area arose in the last years of the twentieth century, and it is in constant progression called Precision Farming. Nowadays, a division of this field growth is relative to Unmanned Aerial Vehicles applications. Most traditional methods employed by farmers are ineffective and do not aid in the progression and solution of these issues. However, there are some fields that have the possibility to enhance many agriculture methods, such fields are Cyber-Physical Systems and Cloud Computing. Given its capabilities like aerial surveillance and mapping, Cyber- Physical Systems like Unmanned Aerial Vehicles are being used to monitor vast crops, to gather insightful data thatwould take a lot more time if being collected by hand. However, these systems typically lack computing power and storage capacity, meaning that much of its gathered data cannot be stored and further analyzed locally. That is the obstacle that Cloud Computing can solve. With the possibility to offload computing power by sending the collected data to a cloud, it is possible to leverage the enormous computing power and storage capabilities of remote data-centers to gather and analyze these datasets. This dissertation proposes an architecture for this use case by leveraging the advantages of Cloud Computing to aid the obstacles of Unmanned Aerial Vehicles. Moreover, this dissertation is a collaboration with an on-going Horizon 2020 European project that deals with precision farming and agriculture enhanced by Cyber-Physical Systems.A cada dia que passa, novas aplicações para Veículos aéreos não tripulados são inventadas, de forma a resolver alguns dos problemas actuais da sociedade. Um desses problemas, é a possibilidade de otimização em processos agrículas. Devido a isto, nos últimos anos do século 20 nasceu uma nova área de investigação intitulada Agricultura de alta precisão. Hoje em dia, uma secção desta área diz respeito à inovação nas aplicações com recurso a Veículos aéreos não tripulados. A maioria dos métodos tradicionais usados por agricultores são ineficientes e não auxiliam nem a evolução nem a resolução destes problemas. Contudo, existem algumas áreas científicas que permitem a evoluçao de algumos métodos agrículas, estas áreas são os Sistemas Ciber-Físicos e a Computação na Nuvem. Dadas as suas capacidades tais como a vigilância e mapeamento aéreo, certos Sistemas Ciber-Físicos como os Veículos aéreos não tripulados estão a ser usados para monitorizar vastas culturas de forma a recolher dados que levariam muito mais tempo caso fossem recolhidos manualmente. No entanto, estes sistemas geralmente não detêm grandes capacidades de computação e armazenamento, o que significa que muitos dos dados recolhidos não podem ser armazenados e analisados localmente. É aí que a Computação na Nuvem é útil, com a possibilidade de enviar estes dados para uma nuvem, é possível aproveitar o enorme poder de computação e os recursos de armazenamento dos datacenters remotos para armazenar e analisar estes conjuntos de dados. Esta dissertação propõe uma arquitetura para este caso de uso ao fazer uso das vantagens da Computação na Nuvem de forma a combater os obstáculos dos Veículos aéreos não tripulados. Além disso, esta dissertação é também uma colaboração com um projecto Europeu Horizonte 2020 na área da Agricultura de alta precisão com recurso a Veículos aéreos não tripulados

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

RF-based automated UAV orientation and landing system

The number of Unmanned Areal Vehicle (UAV) applications is growing tremendously. The most critical applications are operations in use cases like natural disasters and rescue activities. Many of these operations are performed on water scenarios. A standalone niche covering autonomous UAV operation is thus becoming increasingly important. One of the crucial parts of mentioned operations is a technology capable to land an autonomous UAV on a moving platform on top of a water surface. This approach could not be entirely possible without precise UAV positioning. However, conventional strategies that rely on satellite positioning may not always be reliable, due to the existence of accuracy errors given by surrounding environmental conditions, high interferences, or other factors, that could lead to the loss of the UAV. Therefore, the development of independent precise landing technology is essential. The main objective of this thesis is to develop precise landing framework by applying indoor positioning techniques based on RF-anchors to autonomous outdoor UAV operations for cases when a lower accuracy error than the provided by Global Navigation Satellite System (GNSS) is required. In order to analyze the landing technology, a simulation tool was developed. The developed positioning strategy is based on modifications of Gauss-Newton's method, which utilizes as an input parameter the number of anchors, the spacing between them, the initial UAV position, and the Friis-transmission formula to calculate the distance between the anchors and the UAV. As an output, a calculated position of the UAV with an accuracy in the range of tens of centimeters is reached. The simulation campaign shows the dependencies of the effects of the anchor's number and corresponding spacing on positioning accuracy. Also, the simulation campaign shows Gauss-Newton's method parameter value that maximizes the system performance. The results prove that this approach can be applied in a real-life scenario due to achievements of both high accuracy achieved and close to perfect estimated landing trajectory. Keywords: UAV, Positioning, Automatic Landing, Simulatio