Unmanned Aerial Vehicles Localization Research Articles

Power Consumption Minimization of UAV Relay in NOMA Networks

In this letter, we consider employing a non-orthogonal multiple access (NOMA) unmanned aerial vehicle (UAV) as relay to ferry data from a remote base station to multiple ground users. Our objective is to minimize the power consumption of UAV by jointly optimizing the location and transmission power of UAV. The formulated problem is non-convex with highly coupled binary discrete constraints, which is intractable to solve. Firstly, it is transformed into a more tractable form, where the logarithmic constraints are transformed into polynomial form, and the discrete constraints are transformed into continuous form. Then the non-convex equality constraints are handled by penalty method, while the non-convex inequality constraints are handled by successive convex approximation technique. Based on these measures, we propose a double-loop iterative algorithm to obtain the solution approximately. Numerical results show the effectiveness of our proposed algorithm.

Research on target location of unmanned aerial vehicles in parallel path

This paper mainly studies how to use the stereo vision system that combines the monocular vision with parallel path search to locate the target. When the unmanned aerial vehicle (UAV) searches in the mission area according to the parallel path, the SSD image detection algorithm based on deep learning is adopted to detect and identify the target in the area. The image coordinate information is inversely calculated by using the pixel coordinate information fed back by machine vision. The auxiliary coordinate system is established according to the relationship of angle position between the track line and the basic coordinate system in the parallel path. Combining the position relation and the attitude direction information of UAV, the target position conversion relation between the imaging coordinate system and the auxiliary coordinate system is solved by using the direction cosine matrix. Combined with the coordinate information of UAV, the coordinate position of the target point in the basic coordinate system is finally solved through three coordinate conversion operations. In order to avoid the single calculating error of the target coordinates, the weighted average operation is carried out. On the basis of not changing the search trip of the parallel path, the target location function is preliminarily realized through the reverse solution and the weighted average operation of the target coordinates.

Indoor THz SAR Trajectory Deviations Effects and Compensation With Passive Sub-mm Localization System

In radar remote sensing, the Terahertz (THz) spectrum is presently being investigated worldwide with focus on short-range indoor and outdoor applications. The spectrum broadens the unmanned aerial vehicle (UAV) based synthetic aperture radar (SAR) applications to indoor room profiling with sub-mm resolution and material characterization as many materials have unique fingerprints at this spectrum. SAR technique requires precise localization information of the mobile radar sensor, which in conventional SAR is achieved using an existing localization infrastructure, such as a global positioning system (GPS) and inertial measurement unit (IMU). For the indoor THz SAR, the GPS does not provide coverage in indoor complex environments, and also the state-of-art compact IMU does not provide the required sub-mm accuracy. These limitations can be overcome by utilizing an indoor localization system. Therefore, this paper presents an indoor THz simultaneous localization and mapping (SLAM) system. The system comprises of passive tags based radio frequency identification (RFID) localization system and SAR that provides the UAV localization and mapping of the in-room objects. Another challenge for the UAV based indoor THz SAR that is addressed in this paper is motion compensation (MOCO). At the THz, MOCO requires special consideration due to very small trajectory deviation is in the range of carrier wavelength. Therefore, to study the effects of the sub-mm translational errors, a testbed has been set up, and measurement results are presented in this paper along with the 3D electromagnetic simulation results for a carrier frequency of 275 GHz and bandwidth of 50 GHz. Further, to compensate these errors, the sub-mm localization system is used and the results are presented to validate the proposed solution for indoor THz SAR MOCO.

Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

This work investigates the deployment of unmanned aerial vehicles (UAVs) as access points to provide wireless services to users in a green field. Three fundamental deployment designs are studied under practical air-to-ground (ATG) channel models, namely the minimum number of UAVs, their optimal deployment locations and the optimal transmit power allocation. Since these three design goals are coupled, a particle swarm optimization (PSO)-based scheme is proposed in conjunction with the balanced Signal to Interference plus Noise Ratio (SINR) transmit power allocation. By exploiting the closed-form expressions of the SINR-balanced optimal power allocation and the resulting SINR, the proposed PSO-based scheme iteratively optimizes the UAV number and subsequently, their locations until the resulting SINR of each user meets its required minimum value. To improve the convergence behavior of the proposed scheme, two schemes are devised to provide an initial estimate on the minimum number of UAVs by analyzing the system sum-rate capacity before using a K-means clustering technique to initialize the UAV locations. Finally, a power fine-tuning scheme is developed to further reduce the total transmit power. Extensive simulation is performed to confirm the good performance of the proposed schemes.

Robust Autonomous Navigation of Unmanned Aerial Vehicles (UAVs) for Warehouses’ Inventory Application

The inventory inspection using autonomous unmanned aerial vehicles (UAVs) is beneficial in terms of cost, time, and safety of human workers. However, in typical warehouses, it is very challenging for the autonomous UAVs to do inventory task motions safely because aisles are narrow and long, and the illumination is poor. Prior autonomous UAVs are not suitable for such environments, since they suffer from either localization methods prone to disturbance, drift and outliers, or expensive sensors. We present a low-cost sensing system with an extended Kalman filter (EKF)-based multi-sensor fusion framework to achieve practical autonomous navigation of UAVs in warehouse environments. To overcome the inherent drift, outliers, and disturbance problems of naïve UAV localization methods, we suggest 1) exploiting component test of Mahalanobis norm to reject outliers efficiently, 2) introducing pseudo-covariance to incorporate a visual SLAM algorithm, and 3) recognizing floor lanes to get absolute information–as robust data fusion methods. Exemplar results are provided to demonstrate the effectiveness of the methods. The proposed system has been successfully implemented for diverse cyclic inventory inspection tasks in a materials warehouse.

Joint User Association and UAV Location Optimization for UAV-Aided Communications

This letter is concerned with investigating a joint user association and unmanned aerial vehicle (UAV) location optimization problem for UAV-aided communications. Different from the majority of existing studies, this letter aims at deriving solutions to the joint optimization problem that is modeled as a mixed-integer non-convex optimization problem (MINCOP) with a goal of maximizing users’ total achievable data rates. A novel iterative optimization scheme is proposed to handle the MINCOP. Specifically, the MINCOP is first decomposed into an integer optimization subproblem and a non-convex optimization subproblem. An iterative algorithm is then developed to optimize these two separated subproblems alternatively. Besides, the non-convexity of the non-convex subproblem is handled via successive convex approximation. Based on approximate results, Karush-Kuhn-Tucker (KKT) conditions are leveraged to obtain the evolutionary-form solution to the joint optimization problem. The comparison with the state-of-the-art verifies that the developed algorithm is convergent and is able to achieve good performance.

Impact of Hovering Inaccuracy on UAV-Aided RFET

In this letter, the impact of hovering inaccuracy on the performance of unmanned aerial vehicle (UAV)-aided RF energy transfer (RFET) is investigated. Hovering inaccuracy is measured by localization and orientation mismatch of UAV while it hovers above a sensor of interest. An analytical framework is presented that captures these mismatches, and its impact on the performance of UAV-aided RFET is studied. To evaluate the performance, a metric called mismatch index is defined as the ratio of loss in harvested power due to mismatch and harvested power without mismatch. A closed-form expression of the distribution of mismatch index is obtained. A UAV-based experimental setup is developed to collect the data of hovering inaccuracy parameters, and the performance is investigated for three antenna types with different radiation patterns. It is observed that, the optimal deployment height of UAV increases as the antenna becomes more directional.

A modified algorithm for computation issues in UAV-enabled wireless communications

Unmanned aerial vehicles (UAVs) in wireless communication have received significant interests recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. In this paper, we optimize the location of UAVs to maximize their coverage areas. We propose a modified artificial bee colony (MABC) incorporating bee number reallocation and a new search equation. In the proposed algorithm, a greater number of bees in the population are assigned to execute local searches near food sources to enhance solution accuracy, and the bees are guided by elite vectors to enable the algorithm to rapidly converge to a potentially globally optimal position. The deployment of UAVs is thus obtained. The experimental results show that the proposed algorithm achieves great improvements over the traditional algorithm.

Optimal UAV Base Station Trajectories Using Flow-Level Models for Reinforcement Learning

Cellular base stations (BS) and remote radio heads can be mounted on unmanned aerial vehicles (UAV) for flexible, traffic-aware deployment. These UAV base station networks (UAVBSN) promise an unprecendented degree of freedom that can be exploited for spectral efficiency gains as well as optimal network utilization. However, the current literature lacks realistic radio and traffic models for UAVBSN deployment planning and for performance evaluation. In this paper, we propose flow-level models (FLM) for realistically characterizing the UAVBSN performance in terms of a broad range of flow- and system-level metrics. Further, we propose a deep reinforcement learning (DRL) approach that relies on the UAVBSN FLM for learning the optimal traffic-aware UAV trajectories. For a given user traffic density and starting UAV locations, our RL approach learns the optimal UAV trajectories offline that maximizes a cumulative performance metric. We then execute the learned UAV trajectories in a discrete event simulator to evaluate online UAVBSN performance. For ${M}\pmb {=}$ 9 UAVs deployed in a simulated Downtown San Francisco model, where the UAV trajectories are defined by ${N}\pmb {=}$ 20 discrete actions, our approach achieves approximately a three-fold increase in the average user throughput compared to the initial UAV placement, while simultaneously balancing traffic loads across the BSs.

Multi-UAV Dynamic Wireless Networking With Deep Reinforcement Learning

This letter investigates a novel unmanned aerial vehicle (UAV)-enabled wireless communication system, where multiple UAVs transmit information to multiple ground terminals (GTs). We study how the UAVs can optimally employ their mobility to maximize the real-time downlink capacity while covering all GTs. The system capacity is characterized, by optimizing the UAV locations subject to the coverage constraint. We formula the UAV movement problem as a Constrained Markov Decision Process (CMDP) problem and employ Q-learning to solve the UAV movement problem. Since the state of the UAV movement problem has large dimensions, we propose Dueling Deep Q-network (DDQN) algorithm which introduces neural networks and dueling structure into Q-learning. Simulation results demonstrate the proposed movement algorithm is able to track the movement of GTs and obtains real-time optimal capacity, subject to coverage constraint.

Cooperative Localization of a GPS-Denied UAV Using Direction-of-Arrival Measurements

A GPS-denied unmanned aerial vehicle (UAV) (Agent B) is localized through inertial navigation system alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals’ direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored.

Secure Communications for UAV-Enabled Mobile Edge Computing Systems

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users’ transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

Interference modeling and analysis in 3-dimensional directional UAV networks based on stochastic geometry

Abstract In this paper, we model and characterize interference of directional unmanned aerial vehicle (UAV) networks based on stochastic geometry, where each UAV is equipped with a directional antenna and it communicates with another UAV that is located in the three dimensional (3D) space. In particular, the 3D location of UAVs is assumed to be uniformly distributed in a certain volume, which is modeled by Poisson point process. Given a beamwidth, we first design an ideal 3D directional antenna beam pattern with a constant gain of both main-lobe and side-lobe. To model the interference in the UAV network, we analyze the effect of elevation and azimuth between a typical UAV receiver and an interfering UAV transmitter with spherical coordinate system. Then, we investigate distribution of the aggregate interference at a typical UAV receiver from multiple UAVs in terms of side-lobe gain, beamwidth, height of UAV, and distance of a UAV transmitter–receiver pair.

Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

In recent years, unmanned aerial vehicle (UAV) networks have been a focus area of the academic and industrial research community. They have been used in many military and civilian applications. Emergency communication is one of the essential requirements for first responders and victims in the aftermath of natural disasters. In such scenarios, UAVs may configure ad hoc wireless networks to cover a large area. In UAV networks, however, localization and routing are challenging tasks owing to the high mobility, unstable links, dynamic topology, and limited energy of UAVs. Here, we propose swarm-intelligence-based localization (SIL) and clustering schemes in UAV networks for emergency communications. First, we propose a new 3-D SIL algorithm based on particle swarm optimization (PSO) that exploits the particle search space in a limited boundary by using the bounding box method. In the 3-D search space, anchor UAV nodes are randomly distributed and the SIL algorithm measures the distance to existing anchor nodes for estimating the location of the target UAV nodes. Convergence time and localization accuracy are improved with lower computational cost. Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for intercluster distance, intracluster distance, residual energy, and geographic location. For energy-efficient clustering, cluster heads are selected based on improved particle optimization. The proposed SIC outperforms five typical routing protocols regarding packet delivery ratio, average end-to-end delay, and routing overhead. Moreover, SIC consumes less energy and prolongs network lifetime.

Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing.

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.

Self-location of Unmanned Aerial Vehicle Using Conformal Geometric Algebra

Self-location is a major function of unmanned aerial vehicle (UAV). In the absence of satellite navigation signals, a UAV depending merely on a low-accuracy inertial measurement unit (IMU) certainly fails to meet the needs of self-location. This paper introduces a monocular vision-based self-location method of UAV just fitted with a single downward-looking camera. A self-location mathematic model is built in accordance with the collinear relationship among the ground target point, the image point and the optical center of the camera. By taking conformal geometric algebra (CGA) as a mathematic tool, direct operation for geometric elements and geometric relationship is performed along with efficient extension, unary elimination and simplification. Combined with the least square estimation, the spatial location of UAVs can be calculated. Finally, the performance of this approach is verified via a flight test, experimental results show that the UAV location can be obtained with this method, which also embraces significant advantages in the convergence rate and computing time. Furthermore, it can be deployed to embedded devices with limited computing resources, thus it meets the positive real-time requirements of self-location of UAVs.

Target location of unmanned aerial vehicles based on the electro-optical stabilization and tracking platform

Target location is a major function of unmanned aerial vehicles (UAV). In aerial photogrammetry, due to the large object distance, various slight measurement errors will be magnified, resulting in a significant decrease in target location precision. In order to improve the precision of target location, an innovative method of UAV target location is designed. This method utilizes the feature of the airborne electro-optical stabilization and tracking platform (EOSTP) which can track a ground target and keep it in the center of the camera’s field of view to make multiple observations of one target point. The nonlinear system model of the target space position is built according to the geometric relationship between the line-of-sight (LOS) pointing vector and the target point, and then the Cubature Kalman Filter (CKF) algorithm is used to estimate the target position. Considering that CKF algorithm is easily affected by the initial value errors, the initial target position estimation method based on digital elevation model (DEM) approximation is proposed. The validity of the location algorithm is verified via simulation experiments and the results of actual flight test show that this algorithm can improve the location accuracy of ground target in rough terrain area obviously.

Cyber-physical battlefield perception systems based on machine learning technology for data delivery

Data delivery in Cyber-Physical Battlefield Perception Systems(CPBPS) is a challenging task due to the ubiquity locations and the high mobility of node. Due to the special geographical circumstances, communication networks based on fixed infrastructure are unlikely to be established. This paper presents an air-ground coordination communication transmission network, which consists of Unmanned Aerial Vehicle (UAV) subnets and ground vehicle subnets. The UAVs exploit air-to-air (A2A) and air-to-ground (A2G) communication links to assist vehicle communications. However, overreliance on satellite positioning may cause military information to leak. Therefore, we proposed a K-Nearest Neighbor (KNN )combined with genetic algorithms and based on machine learning system (MLS) for data delivery for battlefield environment to realize the privacy protection and guarantee the security with better prediction. The proposed KNN machine learning system can estimate the movement and path of vehicles based on the mobile information obtained. Furthermore, in order to transmit data of UAVs more efficiently, the genetic algorithms (GA) is utilized to determine the relative location of UAVs. Simulation results verify the performance of proposed algorithm.

Radar-Assisted UAV Detection and Identification Based on 5G in the Internet of Things

Unmanned aerial vehicles (UAVs) have broad application potential for the Internet of Things (IoT) due to their small size, low cost, and flexible control. At present, the main positioning method for UAVs is the use of GPS. However, GPS positioning may be affected by stronger electromagnetic signals from spoofing attacks. In this study, a radar-assisted positioning method based on 5G millimeter waves is proposed. In 5G end-to-end network slices, the rotors of UAVs can be detected and identified by deploying 5G millimeter wave radar. High-resolution range profile (HRRP) is used to obtain the UAV location in the detection zone. Micro-Doppler characteristics are used to identify the UAVs and the cepstrum method is used to extract the number and speed information of the UAV rotor. The sinusoidal frequency modulation (SFM) parameter optimization method is used to separate multiple UAVs. The proposed method provides information on the number of UAVs, the position of the UAV, the number of rotors, and the rotation speed of each rotor. The simulation results show that the proposed radar detection method is well suited for UAV detection and identification and provides a valid GPS-independent method for UAV tracking.

Strategic Learning Approach for Deploying UAV-Provided Wireless Services

Unmanned Aerial Vehicle (UAV) have emerged as a promising technique to rapidly provide wireless services to a group of mobile users simultaneously. The paper aims to address a challenging issue that each user is selfish and may misreport his location or preference for changing the optimal UAV location to be close to himself. Using algorithmic game theory, we study how to determine the final location of a UAV in the 3D space, by ensuring all selfish users' truthfulness in reporting their locations for learning purpose. To minimize the social service cost in this UAV placement game, we design strategyproof mechanisms with the approximation ratios, when comparing to the social optimum. We also study the obnoxious UAV placement game to maximally keep their social utility, where each incumbent user may misreport his location to keep the UAV away from him. Moreover, we present the dual-preference UAV placement game by considering the coexistence of the two groups of users above, where users can misreport both their locations and preference types (favorable or obnoxious) towards the UAV. Finally, we extend the three games above to include multiple UAVs and design strategyproof mechanisms with provable approximation ratios.