Efficient Immersive Surveillance of Inaccessible Regions using UAV Network

The number of Unmanned Aerial Vehicles (UAVs) flying around is increasing year after year due to their popularity. This new scenario considerably increases the risk of collision between UAVs, which makes the development of protocols to avoid this issue an emerging topic. This work focuses on the development of a collision avoidance protocol between UAVs performing planned missions. Our proposal is based on the periodic submission of state information, and future position estimates of the UAV, allowing the detection of collision risks. Collision avoidance is achieved by forcing UAVs to stop when they are flying critically near each other, and establishing the priority of the UAV passing by the critical area first; this decision is based on a per-UAV unique identifier. We prove that our solution is robust enough to avoid collisions in all the situations tested; in addition, the overhead introduced by our protocol in terms of additional UAV flight time is low.

The importance of local intervention capability for the assertion of Canadian Sovereignty in the Northwest Passage is recognized. However, Canada lacks the ability to deploy, on demand, assets to search a wide area for rescue or surveillance purposes in the North. This fact motivated our investigation of the feasibility of a rapid intervention system based on a carrier-scouts design in which a number of unmanned aerial vehicles (UAVs) would be transported, air launched and recovered by a carrier aircraft. These UAVs would have the ability to self-organize in formations that correspond to the task at hand. When searching for a target, they would fly in a linear pattern so that the search area swept per hour and the probability of detecting the target would be considerably increased. A 1973 report by the Tactical Combat Aircraft Programs of the Boeing Aerospace Company for the US Air Force [15] and a 2007 thesis by Chalamont [6] indicate that airborne launch and recovery of many UAVs from a carrier aircraft is feasible and requires only already existing technology. We propose here a solution to the remaining problem of managing simultaneously the many UAVs that are required by the vastness of the areas to be surveyed, with a minimum number of human controllers and communications. Namely, we present algorithms for the self-organization of the UAVs in the required formations. These allow for surveillance operations during which close-up images would be acquired of activities in a region of interest, and searching an area for assets in distress and providing a visual presence for such. We reach the conclusion that our proposed local intervention system with flocking UAVs is feasible and would provide a valuable asset for asserting Canadian Sovereignty in the North.

With the rapid development of aviation technology, especially in intelligent situation awareness, autonomous flight control and electric propulsion, the number of unmanned aerial vehicles (UAVs) has grown explosively, bringing challenges to low-altitude flight safety. Autonomous UAV detect and avoid (DAA) technology is a key future direction. Firstly, this paper focuses on UAV multi-sensor data fusion, proposing an asynchronous trajectory fusion method via comprehensive weighting that accounts for multiple influencing factors. Secondly, for trajectory tracking of ownship and intruders, improved Kalman filter (KF) and enhanced Sage-Husa adaptive unscented Kalman filter (AUKF) algorithms are used for vertical and horizontal tracking to enhance accuracy. Then, a hybrid method combining detect and avoid alerting logic for unmanned aircraft systems (DAIDALUS) with Markov decision process (MDP) is proposed to solve the UAV collision avoidance decision-making problem. It calculates collision avoidance strategies via the state space, takes them as MDP’s action space, sets up reward functions and state transition probabilities to build an MDP model, and explores the influence of discount factors. Finally, through experimental tests, results show the proposed UAV detection and avoidance decision method has a 27.2% efficiency increase compared to standalone DAIDALUS, the loss of well clear (LoWC) rate is only 5.8%, and the nearest distance between the intruder and the ownship is 343 m, meeting the collision avoidance requirements for UAV flight.

To enable safe and efficient operation of a large number of Unmanned Aerial Vehicles (UAVs) as envisioned in future commercial operations, a novel networked-based air traffic control paradigm is needed to provide for autonomous route optimization and cooperation to enable deconfliction. This system would need to ensure deconfliction from nearby manned and unmanned operations and would need the capability to respond to unexpected close-proximity aircraft, known as interlopers. This network-based control paradigm will require that operators be able to oversee a network of cooperative and autonomous UAVs capable of maneuvering at low altitudes. Such a network would need new algorithms and increased autonomy to best support cooperative deconfliction and high level supervisory control, and one that also allows operators the ability to both understand local and global behavior of a network of UAVs. To this end, we propose a paradigm-shifting multi-layer distributed air traffic control concept focused on low altitude, high density UAV operations. This new architecture would include a group of analysts that perform a global analysis of the UAV network at a strategic level and more tactical, local controllers manipulating the UAV network, who are advised by the analysts. The proposed operational support system would include methods for the automatic identification of issues within the network, as well as a method for the automatic distribution of effective tasks to the most suitable personnel. This architecture would be a combination of current air traffic control techniques as well as those troubleshooting techniques found in other domains like network and cloud computing management.

With the development of unmanned platforms, there are large numbers of unmanned aerial vehicles(UAVs), due to UAVs’ new features such as lightweight, high speed mobility and heterogeneous, UAVs are facing great challenges of data security. As a key means to protect data security, cryptography is of great significance. Traditional mature and complete cryptography schemes need to be adjusted to meet the new features of UAVs. We start with the security challenges faced by UAV architecture, then analyze the cryptographic technologies that can be applied. By comparing with existing researching schemes, we shows the advantages of different cryptographic technologies in the security protection of unmanned platforms and looks forward to the future.

The problem of assessing the quality of equipment of different unmanned aerial vehicles groups types based on fuzzy sets is considered. The growth in the number of unmanned aerial vehicles for various purposes and the expansion of their application in heterogeneous groups raises the question of developing new criteria for the effectiveness of their use. A new approach to increase the productivity of registration and information processing operations by determining the image quality based on the video camera's angle of inclination, altitude and flight speed of the unmanned aerial vehicle is proposed. Since the input information about the state of the scene and data from unmanned aerial vehicle sensors may be incomplete and poorly structured, the article proposes to apply the concept of granular calculations and fuzzy sets to build an air reconnaissance database. A new mathematical model (information granule) is proposed based on fuzzy data on the altitude and speed of flight and the angle of the camera, which allows obtaining a forecast of image quality that can be gotten by unmanned aerial vehicles while traveling along the route.

In this paper, a novel framework for power-efficient, cluster- based machine-to-machine (M2M) communications is proposed. In the studied model, a number of unmanned aerial vehicles (UAVs) are used as aerial base stations to collect data from the cluster heads (CHs) of a set of M2M clusters. To minimize the CHs' transmit power while satisfying the rate requirements of M2M devices, an optimal scheduling and resource allocation mechanism for CH-UAV communications is proposed. First, using the queue rate stability concept, the minimum number of UAVs as well as the dwelling time that each UAV must spend for servicing the CHs are computed. Next, the optimal resource allocation for the CH-UAV communication links is determined such that M2M devices rate requirements are satisfied with a minimum transmit power. Simulation results show that, as the packet transmission probability of machines increases, the minimum number of UAVs required to guarantee the queue rate stability of CHs will also significantly increase. Our results also show that, compared to a case with pre-deployed terrestrial base stations, the average transmit power of CHs will decrease by 68% when UAVs are used.

The increase in the number of Unmanned Aerial Vehicles (UAVs) for liquid transport tasks, such as agricultural spraying, results in significant stability challenges. The free movement of the liquid, known as sloshing, generates unpredictable forces that destabilize the vehicle and increase collision risks. This study treats this problem by developing and validating a nonlinear control strategy to ensure precise trajectory tracking while actively suppressing liquid sloshing. The coupled dynamics of the system are modeled using the Euler–Lagrange formalism by representing the UAV as a planar vertical take-off and landing (PVTOL) aircraft and the liquid sloshing dynamics as an equivalent pendulum model. The stability of the entire closed-loop system is proven using Lyapunov’s direct method. The analytical results are validated through numerical simulations in MATLAB/Simulink, which demonstrate excellent tracking of desired altitude and horizontal trajectories. Crucially, the simulations confirm that the controller effectively attenuates the sloshing oscillations, offering a robust solution to enhance the safety and operational performance of UAVs in liquid transport applications.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

Recently, the use of unmanned aerial vehicles for both reconnaissance and fire strikes has been increasing. The control of such vehicles and the transmission of telemetry information are carried out using a combined command and telemetry radio link and frequency hopping spread spectrum technology. In such conditions, the task of developing automatic algorithms for detecting and separating radio links for portable radio monitoring devices becomes especially relevant. The aim of this work is to extend the capabilities of portable radio monitoring devices to detect unmanned aerial vehicles’ radio signals in sophisticated electronic environment by automating signal processing. Formally, the research task is to detect and separate several frequency hopping spread spectrum signals in same frequency band. To solve this task, it is first necessary to detect the frequency channels and estimate frequency and, if necessary, temporal and modulation parameters of the signals in these channels. Estimates of signal parameters values are recorded in associative arrays. The paper proposes a scheme of the algorithm for automatic detection and separation of signals with frequency hopping spread spectrum, as well as the structure of the array with the results of frequency band analysis. In the study of developed algorithm, we considered the practically important option of separating radio links with frequency hopping spread spectrum by the spectral width of the frequency element and the value of the signal-to-noise ratio in the frequency channel, since these features can be quite easily distinguished in the frequency domain. The radio links separation was performed by cluster analysis of scatter plots of parameter pairs estimates using a Gaussian mixture model. The developed algorithm can be implemented in existing and prospective portable radio monitoring devices to automatically detect and determine the number of unmanned aerial vehicles by their radio links signals, in particular, under conditions of a priori uncertainty about the values of signal parameters.

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

This paper investigates the issue of spectrum collaboration under dynamic changes in the number of unmanned aerial vehicle (UAV) swarms under jamming conditions. We focus on the scenario where multiple UAV swarms conduct reconnaissance tasks, the head UAV (H-UAV) of each swarm relays the detected information gathered by the swarm back to the ground control station (GCS). To achieve spectrum collaboration among H-UAVs, first, we model the problem as an optimization that maximizes the sum of the achievable rates of H-UAVs to GCS. Then, we treat H-UAVs as agents and adopt the classic multi-agent proximal policy optimization (MAPPO) for centralized training of the agents. By setting the number of agents to a specific value, we make it applicable for distributed spectrum collaboration scenarios with dynamic changes in the number of UAV swarms. Finally, we measure the effectiveness of the algorithm by the spectrum conflict occurrences. Simulation results show that the proposed algorithm can significantly reduce the number of spectrum conflicts among H-UAVs, achieving effective spectrum collaboration under dynamic changes in the number of UAV swarms in jamming conditions.

The number of Unmanned Aerial Vehicle (UAV) applications is growing tremendously. The most critical ones are operations in use cases such as natural disasters, and search 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 surface vessel. This approach could not be entirely possible without precise UAV positioning. However, conventional strategies that rely on satellite localization may not always be reliable, due to scenario specifics. Therefore, the development of an independent precise landing technology is essential. In this paper, we developed the localization and landing system based on Gauss-Newton’s method, which allows to achieve the required localization accuracy.

In recent years there has been an increase in the number of unmanned aerial vehicle (UAV) applications intended for various missions in a variety of environments. The adoption of the more‐electric aircraft has led to a greater emphasis on electrical power systems (EPS) for safe flight through an increased number of critical loads being sourced with electrical power. Despite extensive literature detailing the development of systems to detect UAV failures and enhance overall system reliability, few have focussed directly on the increasingly complex and dynamic EPS. This study outlines the development of a novel UAV EPS fault classification and diagnostic (FCD) system based on hidden Markov models (HMM) that will assist and improve EPS health management and control. The ability of the proposed FCD system to autonomously detect, classify and diagnose the severity of diverse EPS faults is validated with development of the system for NASA's advanced diagnostic and prognostic testbed (ADAPT), a representative UAV EPS system. EPS data from the ADAPT network was used to develop the FCD system and results described within this study show that a high classification and diagnostic accuracy can be achieved using the proposed system.

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV’s localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.