A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

Recent technological advancements in space, air and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, the real-world deployment of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the space and terrestrial components, UAVs are expected to meet performance requirements with high flexibility and dynamics using limited resources. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. In this paper, we provide a comprehensive review of recent learning-based algorithmic approaches. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit (MAB), particle swarm optimization (PSO) and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, which can be applicable to various UAV-assisted missions on a SAGIN using these algorithms. We simulate users and environments according to real-world scenarios and compare the learning-based and PSO-based methods in terms of throughput, load, fairness, computation time, etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional (3D) variations of these algorithms to reflect different deployment cases. Our simulation suggests that the $3$D satisfaction-based learning algorithm outperforms the other approaches for various metrics in most cases. We discuss some open challenges at the end and our findings aim to provide design guidelines for algorithm selections while optimizing the deployment of UAV-assisted SAGINs.Comment: Submitted to the IEEE Internet of Things Journal in June 202

Wide-Area Surveillance System using a UAV Helicopter Interceptor and Sensor Placement Planning Techniques

This project proposes and describes the implementation of a wide-area surveillance system comprised of a sensor/interceptor placement planning and an interceptor unmanned aerial vehicle (UAV) helicopter. Given the 2-D layout of an area, the planning system optimally places perimeter cameras based on maximum coverage and minimal cost. Part of this planning system includes the MATLAB implementation of Erdem and Sclaroff’s Radial Sweep algorithm for visibility polygon generation. Additionally, 2-D camera modeling is proposed for both fixed and PTZ cases. Finally, the interceptor is also placed to minimize shortest-path flight time to any point on the perimeter during a detection event. Secondly, a basic flight control system for the UAV helicopter is designed and implemented. The flight control system’s primary goal is to hover the helicopter in place when a human operator holds an automatic-flight switch. This system represents the first step in a complete waypoint-navigation flight control system. The flight control system is based on an inertial measurement unit (IMU) and a proportional-integral-derivative (PID) controller. This system is implemented using a general-purpose personal computer (GPPC) running Windows XP and other commercial off-the-shelf (COTS) hardware. This setup differs from other helicopter control systems which typically use custom embedded solutions or micro-controllers. Experiments demonstrate the sensor placement planning achieving \u3e90% coverage at optimized-cost for several typical areas given multiple camera types and parameters. Furthermore, the helicopter flight control system experiments achieve hovering success over short flight periods. However, the final conclusion is that the COTS IMU is insufficient for high-speed, high-frequency applications such as a helicopter control system

Management: A continuing literature survey with indexes, March 1976

Management is a compilation of references to selected reports, journal articles, and other documents on the subject of management. This publication lists 368 documents originally announced in the 1975 issues of Scientific and Technical Aerospace Reports (STAR) or International Aerospace Abstracts (IAA). It includes references on the management of research and development, contracts, production, logistics, personnel, safety, reliability and quality control. It also includes references on: program, project and systems management; management policy, philosophy, tools, and techniques; decisionmaking processes for managers; technology assessment; management of urban problems; and information for managers on Federal resources, expenditures, financing, and budgeting

Design of autonomous sustainable unmanned aerial vehicle - A novel approach to its dynamic wireless power transfer

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Electric UAVs are presently being used widely in civilian duties such as security, surveillance, and disaster relief. The use of Unmanned Aerial Vehicle (UAV) has increased dramatically over the past years in different areas/fields such as marines, mountains, wild environments. Nowadays, there are many electric UAVs development with fast computational speed and autonomous flying has been a reality by fusing many sensors such as camera tracking sensor, obstacle avoiding sensor, radar sensor, etc. But there is one main problem still not able to overcome which is power requirement for continuous autonomous operation. When the operation needs more power, but batteries can only give for 20 to 30 mins of flight time. These types of system are not reliable for long term civilian operation because we need to recharge or replace batteries by landing the craft every time when we want to continue the operation. The large batteries also take more loads on the UAV which is also not a reliable system. To eliminate these obstacles, there should a recharging wireless power station in ground which can transmit power to these small UAVs wirelessly for long term operation. There will be camera attached in the drone to detect and hover above the Wireless Power Transfer device which got receiving and transmitting station can be use with deep learning and sensor fusion techniques for more reliable flight operations. This thesis explores the use of dynamic wireless power to transfer energy using novel rotating WPT charging technique to the UAV with improved range, endurance, and average speed by giving extra hours in the air. The hypothesis that was created has a broad application beyond UAVs. The drone autonomous charging was mostly done by detecting a rotating WPT receiver connected to main power outlet that served as a recharging platform using deep neural vision capabilities. It was the purpose of the thesis to provide an alternative to traditional self-charging systems that relies purely on static WPT method and requires little distance between the vehicle and receiver. When the UAV camera detect the WPT receiving station, it will try to align and hover using onboard sensors for best power transfer efficiency. Since this strategy relied on traditional automatic drone landing technique, but the target is rotating all the time which needs smart approaches like deep learning and sensor fusion. The simulation environment was created and tested using robot operating system on a Linux operating system using a model of the custom-made drone. Experiments on the charging of the drone confirmed that the intelligent dynamic wireless power transfer (DWPT) method worked successfully while flying on air

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

NASA Capability Roadmaps Executive Summary

This document is the result of eight months of hard work and dedication from NASA, industry, other government agencies, and academic experts from across the nation. It provides a summary of the capabilities necessary to execute the Vision for Space Exploration and the key architecture decisions that drive the direction for those capabilities. This report is being provided to the Exploration Systems Architecture Study (ESAS) team for consideration in development of an architecture approach and investment strategy to support NASA future mission, programs and budget requests. In addition, it will be an excellent reference for NASA's strategic planning. A more detailed set of roadmaps at the technology and sub-capability levels are available on CD. These detailed products include key driving assumptions, capability maturation assessments, and technology and capability development roadmaps