An unmanned aerial vehicle (UAV) can be used for wireless communication and localization, among many other things. When terrestrial networks are either damaged or non-existent, and the area is GPS-denied, the UAV can be quickly deployed to provide communication and localization services to ground terminals in a specific target area. In this study, we propose an UAV operation model for unified communication and localization using reinforcement learning (UCL-RL) in a suburban environment which has no cellular communication and GPS connectivity. First, the UAV flies to the target area, moves in a circular fashion with a constant turning radius and sends navigation signals from different positions to the ground terminals. This provides a dynamic environment that includes the turning radius, the navigation signal transmission points, and the height of the unmanned aerial vehicle as well as the location of the ground terminals. The proposed model applies a reinforcement learning algorithm where the UAV continuously interacts with the environment and learns the optimal height that provides the best communication and localization services to the ground terminals. To evaluate the terminal position accuracy, position dilution of precision (PDOP) is measured, whereas the maximum allowable path loss (MAPL) is measured to evaluate the communication service. The simulation result shows that the proposed model improves the localization of the ground terminals while guaranteeing the communication service.

Unmanned aerial vehicle (UAV)-assisted systems have attracted a lot of attention due to its high probability of line-of-sight (LoS) connections and flexible deployment. In this paper, we aim to minimize the upload time required for the UAV to collect information from the sensor nodes in disaster scenario, while optimizing the deployment position of UAV. In order to get the deployment solution quickly, a data-driven approach is proposed in which an optimization strategy acts as the expert. Considering that images could capture the spatial configurations well, we use a convolutional neural network (CNN) to learn how to place the UAV. In the end, the simulation results demonstrate the effectiveness and generalization of the proposed method. After training, our CNN can generate UAV configuration faster than the general optimization-based algorithm.

To localize an unknown wave source in non-line-of-sight environments, a wave source localization scheme using multiple unmanned-aerial-vehicles (UAVs) is proposed. In this scheme, each UAV estimates the direction-of-arrivals (DoAs) of received signals and the wave source is localized from the estimated DoAs by means of maximum likelihood estimation. In this study, by extending the concept of this scheme, we propose a novel wave source localization scheme using a single UAV. In the proposed scheme, the UAV moves on the path comprising multiple measurement points and the wave source is sequentially localized from DoA distributions estimated at these measurement points. At each measurement point, with a moving path planning algorithm, the UAV determines the next measurement point from the estimated DoA distributions and measurement points that the UAV has already visited. We consider two moving path planning algorithms, and validate the proposed scheme through simulation experiments.

In this paper, a wireless communication network that uses unmanned aerial vehicles (UAVs) in the sky to transmit information between ground users is considered. We highlight a delay-tolerant network, where information is relayed in a store-and-forward fashion by establishing two types of intermittent communication links: between a UAV and a user (UAV-to-user) and between UAVs (UAV-to-UAV). Thus, a flight algorithm that controls the movement of the UAVs is crucial in achieving rapid information transmission. Our study proposes new flight algorithms that simultaneously consider the two types of communication links. In UAV-to-UAV links, the direct information transmission between two UAVs and the indirect transmission through other UAVs are considered separately. The movement of the UAVs is controlled by solving an optimization problem at certain time intervals, with a variable consideration ratio of the two types of links. In addition, we investigate not only the case where all UAVs move cooperatively but also the case where each UAV moves autonomously. Simulation results show that the proposed algorithms are effective. Moreover, they indicate the existence of an optimal consideration ratio of the two types of communication and demonstrate that our approach enables the control of frequencies of establishing the communication links. We conclude that increasing the frequency of indirect communication between UAVs improves network performance.

This paper considers an emergency communication system controlling multiple unmanned aerial vehicles (UAVs) in the sky over a large-scale disaster-affected area. This system is based on delay-tolerant networking, and information from ground users is relayed by the UAVs through wireless transmission and the movement of UAVs in a store-and-forward manner. Each UAV moves autonomously according to a predetermined flight method, which uses the positions of other UAVs through communication. In this paper, we propose a new method for UAV flight considering the non-uniformity of user distributions. The method is based on the Voronoi cell using the predicted locations of other UAVs. We evaluate the performance of the proposed method through computer simulations with a non-uniform user distribution generated by a general cluster point process. The simulation results demonstrate the effectiveness of the proposed method.

In this manuscript, we propose a joint channel and power assignment algorithm for an unmanned aerial vehicle (UAV) swarm communication system based on multi-agent deep reinforcement learning (DRL). Regarded as an agent, each UAV to UAV (U2U) link can choose the optimal channel and power according to the current situation after training is successfully completed. Further, a mixing network is introduced based on DRL, where Q values of every single agent are non-linearly mapped, and we call it the QMIX algorithm. As it accesses state information, QMIX can learn to enrich the joint action value function. The proposed method can be used for both unicast and multicast scenarios. Experiments show that each U2U link can be trained to meet the constraints of UAV communication and minimize the interference to the system. For unicast communication, the communication rate is increased up to 15.6% and 8.9% using the proposed DRL method compared with the well-known random and adaptive methods, respectively. For multicast communication, the communication rate is increased up to 6.7% using the proposed QMIX method compared with the DRL method and 13.6% using DRL method compared with adaptive method. Besides, the successful transmission probability can maintain a high level.

In this paper, we propose a novel Hybrid-Hierarchical spatial-aerial-Terrestrial Edge-Centric (H2TEC) for the space-air integrated Internet of Things (IoT) networks. (H2TEC) comprises unmanned aerial vehicles (UAVs) that act as mobile fog nodes to provide the required services for terminal nodes (TNs) in cooperation with the satellites. TNs in (H2TEC) offload their generated tasks to the UAVs for further processing. Due to the limited energy budget of TNs, a novel task allocation protocol, named TOP, is proposed to minimize the energy consumption of TNs while guaranteeing the outage probability and network reliability for which the transmission rate of TNs is optimized. TOP also takes advantage of the energy harvesting by which the low earth orbit satellites transfer energy to the UAVs when the remaining energy of the UAVs is below a predefined threshold. To this end, the harvested power of the UAVs is optimized alongside the corresponding harvesting time so that the UAVs can improve the network throughput via processing more bits. Numerical results reveal that TOP outperforms the baseline method in critical situations that more power is required to process the task. It is also found that even in such situations, the energy harvesting mechanism provided in the TOP yields a more efficient network throughput.

In wireless links between ground stations and UAVs (Unmanned Aerial Vehicles), wireless signals may be attenuated by obstructions such as buildings. A three-dimensional RSS (Received Signal Strength) map (3D-RSS map), which represents a set of RSSs at various reception points in a three-dimensional area, is a promising geographical database that can be used to design reliable ground-to-air wireless links. The construction of a 3D-RSS map requires higher computational complexity, especially for a large 3D area. In order to sequentially estimate a 3D-RSS map from partial observations of RSS values in the 3D area, we propose a graph Laplacian-based sequential smooth estimator. In the proposed estimator, the 3D area is divided into voxels, and a UAV observes the RSS values at the voxels along a predetermined path. By considering the voxels as vertices in an undirected graph, a measurement graph is dynamically constructed using vertices from which recent observations were obtained and their neighboring vertices, and the 3D-RSS map is sequentially estimated by performing graph Laplacian regularized least square estimation.

The reconnaissance mode with the cooperation of two unmanned aerial vehicles (UAVs) equipped with airborne visual tracking platforms is a common practice for localizing a target. Apart from the random noises from sensors, the localization performance is much dependent on their cooperative trajectories. In our previous work, we have proposed a cooperative trajectory generating method that proves better than EKF based method. In this letter, an improved online trajectory generating method is proposed to enhance the previous one. First, the least square estimation method has been replaced with a geometric-optimization based estimation method, which can obtain a better estimation performance than the least square method proposed in our previous work; second, in the trajectory optimization phase, the position error caused by estimation method is also considered, which can further improve the optimization performance of the next way points of the two UAVs. The improved method can well be applied to the two-UAV trajectory planning for corporative target localization, and the simulation results confirm that the improved method achieves an obviously better localization performance than our previous method and the EKF-based method.

In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

In the typical unmanned aircraft system (UAS), several unmanned aerial vehicles (UAVs) traveling at a velocity of 40-100km/h and with altitudes of 150-1,000m will be used to cover a wide service area. Therefore, Doppler shifts occur in the carrier frequencies of the transmitted and received signals due to changes in the line-of-sight velocity between the UAVs and the terrestrial terminal. By observing multiple Doppler shift values for different UAVs or observing a single UAV at different local times, it is possible to detect the user position on the ground. We conducted computer simulations for evaluating user position detection accuracy and Doppler shift distribution in several flight models. Further, a positioning accuracy index (PAI), which can be used as an index for position detection accuracy, was proposed as the absolute value of cosine of the inner product between two gradient vectors formed by Doppler shifts to evaluate the relationship between the location of UAVs and the position of the user. In this study, a maximum positioning error estimation method related to the PAI is proposed to approximate the position detection accuracy. Further, computer simulations assuming a single UAV flying on the curved routes such as sinusoidal routes with different cycles are conducted to clarify the effectiveness of the flight route in the aspects of positioning accuracy and latency by comparing with the conventional straight line fight model using the PAI and the proposed maximum positioning error estimation method.

This paper proposes a single coil active shielding method of wireless unmanned aerial vehicle charger for leakage magnetic field reduction. A proposed shielding system eliminates the leakage magnetic field generated from the transmitting and receiving coils by generating the cancelling magnetic field. In order to enhance shielding effectiveness and preserve power transfer efficiency, shielding coil design parameters including radius and turns will analyze. Based on the analysis of coil design, shielding effectiveness and power transfer efficiency will estimate. In addition, shielding current control method corresponding to leakage magnetic field strength and phase will describe. A proposed shielding system has verified by simulations and experiments in terms of the total shielding effectiveness and power transfer efficiency measurements. The simulation and experimental results show that a proposed active shielding system has achieved 68.85% of average leakage magnetic field reduction with 1.92% of power transfer efficiency degradation. The shielding effectiveness and power transfer efficiency variation by coil design has been experimentally verified.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.

Unmanned aerial vehicle (UAV) communication has drawn rising interest recently with the distinctive gains brought by its inherent mobility. In this paper, we investigate the throughput maximization problem in UAV-enabled uplink communication, where multiple ground nodes communicate with a UAV while a group of ground jammers send jamming signals to jam the communications between UAV and the ground nodes. In contrast to the previous works that only considering UAV's transmit power allocation and two-dimension (2D) trajectory design, the ground nodes' transmit power allocation and scheduling along with the UAV's three-dimensional (3D) trajectory design are jointly optimized. The formulated throughput maximization problem is a mixed-integer non-convex programme that hard to be solved in general. Thus, we propose an iterative algorithm to make the problem trackable by applying the block coordinate descent and successive convex optimization techniques. Simulation results show that our proposed algorithm outperforms the benchmark methods that improving the throughput of the system significantly.

We investigate the uplink channel selection problem of unmanned aerial vehicle (UAV)-aided data collection system in delay-sensitive sensor networks. In the studied model, the fixed-wing UAV is dispatched to gather sensing information from terrestrial sensor nodes (SNs) and they contend for uplink channels for transmission. With the goal of minimizing the system-wide delay, we formulate a resource allocation problem. Encountered with the challenge that the flight trajectory of UAV is unknown to SNs and the wireless channel is time-varying, we solve the problem by stochastic game approach and further propose a fully distributed channel selection algorithm which is proved to converge to a pure strategy Nash Equilibrium (NE). Simulation results are presented to show that our proposed algorithm has good performance.

Unmanned aircraft systems (UASs) have been developed and studied as temporal communication systems for emergency and rescue services during disasters, such as earthquakes and serious accidents. In a typical UAS model, several unmanned aerial vehicles (UAVs) are used to provide services over a large area. The UAV is comprised of a transmitter and receiver to transmit/receive the signals to/from terrestrial stations and terminals. Therefore, the carrier frequencies of the transmitted and received signals experience Doppler shifts due to the variations in the line-of-sight velocity between the UAV and the terrestrial terminal. Thus, by observing multiple Doppler shifts from different UAVs, it is possible to detect the position of a user that possesses a communication terminal for the UAS. This study aims to present a methodology for position detection based on the least-squares method to the Doppler shift frequencies. Further, a positioning accuracy index is newly proposed, which can be used as an index for measuring the position accurately, instead of the dilution-of-precision (DOP) method, which is used for global positioning systems (GPSs). A computer simulation was conducted for two different flight route models to confirm the applicability of the proposed positioning method and the positioning accuracy index. The simulation results confirm that the parameters, such as the flight route, the initial position, and velocity of the UAVs, can be optimized by using the proposed positioning accuracy index.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

In consideration of the image noise captured by photoelectric cameras at nighttime, a robust motion detection algorithm based on sparse representation is proposed in this study. A universal dictionary for arbitrary scenes is presented. Realistic and synthetic experiments demonstrate the robustness of the proposed approach.

The 4 lowest Transverse-Electric modes of a cylindrical Dielectric Resonator Antenna were investigated using a commercially available simulation software. All 4 modes were shown to produce dipole or multi-pole radiation patterns, having Transverse-Electric polarization as opposed to Transverse-Magnetic as with conventional wire antennas. The even numbered modes were shown to be applicable to the niche application of small Unmanned Aerial Vehicles to ground station communications. A practical design for the lowest order even mode was prepared, and successfully demonstrated on a carbon fiber reinforced plastic ground plane. That design was then shown in simulation to have less adverse interaction when installed on a common small Unmanned Aerial Vehicle airframe at the new 5.05GHz telemetry band than an off-airframe dipole.