Unmanned Aerial Vehicles Localization Research Articles

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively.

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs.

At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.

A new connectivity model for unmanned aerial vehicle communications and flying height optimization

AbstractThis paper presents a new connectivity model for unmanned aerial vehicle (UAV) communications. We present closed‐form formulas for the probability of line‐of‐sight as a function of UAV height and distance to a ground based receiver, based on a two‐dimensional model of building and UAV locations. We show that our formulas exactly match Monte‐Carlo simulated values, whereas the existing approach over‐predicts by a factor of 2 to 3. We use the model to optimize the flying height for a UAV deployed as a mobile base station, where the goal is to maximize the number of possible connected users. We observe that the optimal height has a log‐linear characteristic. We use this to present a closed‐form formula for optimal height. We show that our techniques predict the height within 4% of realistic Monte‐Carlo simulations, and significantly outperform existing approaches which are shown to over‐predict by 40%–50%.

A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.

UAV Trajectory Design and Power Optimization for Terahertz Band-Integrated Sensing and Communications

Sixth generation (6G) wireless networks require very low latency and an ultra-high data rate, which have become the main challenges for future wireless communications. To effectively balance the requirements of 6G and the extreme shortage of capacity within the existing wireless networks, sensing-assisted communications in the terahertz (THz) band with unmanned aerial vehicles (UAVs) is proposed. In this scenario, the THz-UAV acts as an aerial base station to provide information on users and sensing signals and detect the THz channel to assist UAV communication. However, communication and sensing signals that use the same resources can cause interference with each other. Therefore, we research a cooperative method of co-existence between sensing and communication signals in the same frequency and time allocation to reduce the interference. We then formulate an optimization problem to minimize the total delay by jointly optimizing the UAV trajectory, frequency association, and transmission power of each user. The resulting problem is a non-convex and mixed integer optimization problem, which is challenging to solve. By resorting to the Lagrange multiplier and proximal policy optimization (PPO) method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, given the UAV location and frequency, the sub-problem of the sensing and communication transmission powers is transformed into a convex problem, which is solved by the Lagrange multiplier method. Second, in each iteration, for given sensing and communication transmission powers, we relax the discrete variable to a continuous variable and use the PPO algorithm to tackle the sub-problem of joint optimization of the UAV location and frequency. The results show that the proposed algorithm reduces the delay and improves the transmission rate when compared with the conventional greedy algorithm.

Bayesian quickest detection‐based spectrum sensing for cognitive UAV networks

AbstractWith the gradual popularization of unmanned aerial vehicles (UAVs), spectrum resources are increasingly scarce. At this time, cognitive radio (CR) technology came into being, which can provide available spectrum resources for UAVs. However, different from the conventional CR networks, the UAV's location flexibility significantly reduces the spectrum sensing performance and increases the detection delay. Therefore, we propose a Bayesian quickest detection‐based spectrum sensing for cognitive UAV networks in this paper. To this aim, we establish a multislot spectrum sensing model in a cognitive UAV network to realize cooperative spectrum sensing (CSS) within the single UAV and avoid disadvantages of cooperation among multiple UAVs. On the basis of this, we make use of Bayesian quickest detection to implement multislot spectrum sensing and evaluate its false alarm probability and detection delay. Further, the tradeoff problem between the false alarm and the detection delay is developed and converted into an optimization stopping time problem. Subsequently, the stopping time problem is theoretically proved and solved. Finally, compared to Bayesian detection, numerical simulation results show that the superiority of Bayesian quickest detection with respect to the false alarm probability and the detection delay is evident, which proves the correctness and effectiveness of the proposed tradeoff problem.

AI-Based Mobility-Aware Energy Efficient Resource Allocation and Trajectory Design for NFV Enabled Aerial Networks

In this paper, we propose a novel joint intelligent trajectory design and resource allocation algorithm based on users’ mobility and their requested services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act as nodes of a network function virtualization (NFV) enabled network. Our objective is to maximize energy efficiency and minimize the average delay on all services by allocating the limited radio and NFV resources. In addition, due to the traffic conditions and mobility of users, we let some virtual network functions (VNFs) migrate from their current locations to other locations to satisfy the Quality of Service requirements. We formulate our problem to find near-optimal locations of UAVs, transmit power, subcarrier assignment, VNF placement, and VNF scheduling over the UAVs and perform suitable VNF migration. Then we propose a novel hierarchical hybrid continuous and discrete action (HHCDA) deep reinforcement learning method to solve the proposed problem. Finally, the convergence and computational complexity of the proposed algorithm and its performance is analyzed for different parameters. Simulation results show that our proposed HHCDA method decreases the average end-to-end delay by 31.5% and increases the energy efficiency by 40% compared to the state-of-the-art Deep Deterministic Policy Gradient method.

Perception-Aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This work proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using four-dimensional (4D) building information models (BIM), considering the project progress, safety requirements, and UAV’s localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.

Learning-Based Beam Alignment for Uplink mmWave UAVs

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

A Location-Aware Resource Optimization for Maximizing Throughput of Emergency Outdoor–Indoor UAV Communication with FSO/RF

In emergency communication scenarios, unmanned aerial vehicles (UAVs) can be used as an air relay to provide higher-quality communication for indoor users. When bandwidth resources are scarce, the use of free space optics (FSO) technology will greatly improve the resource utilization of the communication system. Therefore, we introduce FSO technology into the backhaul link of outdoor communication, and use free space optical/radio frequency (FSO/RF) technology to realize the access link of outdoor indoor communication. The deployment location of UAVs will affect not only the through wall loss of outdoor-indoor communication but also the quality of FSO communication, and, therefore, it needs to be optimized. In addition, by optimizing the power and bandwidth allocation of UAVs, we realize the efficient utilization of resources and improve the system throughput on the premise of considering information causality constraints and user fairness. The simulation results show that, by optimizing the location and power bandwidth allocation of UAVs, the system throughput is maximized, and the throughput between each user is fair.

A UAV-based explore-then-exploit system for autonomous indoor facility inspection and scene reconstruction

Traditional indoor facility inspections on pipelines and boilers are conducted manually and can be logistically challenging, labor-intensive, costly, and dangerous for the inspectors. With the maturity of unmanned technology, the unmanned aerial vehicle (UAV) is becoming a promising alternative to the problematic manual inspection. However, due to the lack of GPS signal indoor, the localization of UAVs is a big challenge to achieve fully autonomous inspection. Moreover, the narrow and complex indoor environment makes it difficult to guarantee flight safety. This paper presents a UAV-based explore-then-exploit system to tackle these problems for autonomous indoor facility data collection and scene reconstruction. The proposed system consists of a hardware description and integration of two UAVs, a two-step simultaneous localization and mapping (SLAM) method for UAV localization and 3D environmental mapping, a safety-guaranteed coverage path planning algorithm for inspection and data collection, as well as an obstacle-aware trajectory generation method. The proposed system is examined in GPS-denied and cluttered indoor environment and 3D scene reconstruction is conducted. The quantitative analysis shows that the positioning accuracy is centimeter-level and the reconstruction error is within 3 cm. The performance analysis demonstrates the robustness and feasibility of our system in reconstructing and inspecting complex indoor environments for high-efficiency and low-cost facility management.

Coarse-to-fine visual autonomous unmanned aerial vehicle landing on a moving platform

Autonomous unmanned aerial vehicle (UAV) landing is a challenging task, especially on a moving platform in an unstructured environment. Under such a scenario, successful UAV landing is mainly affected by poor UAV localization performance. To solve this problem, we propose a coarse-to-fine visual autonomous UAV landing system based on an enhanced visual positioning approach. The landing platform is marked with a specially designed QR code marker, which is developed to improve the landing accuracy when the UAV approaches the landing site. Besides, we employ the you only look once framework to enhance the visual positioning accuracy, thereby promoting the landing platform detection when the UAV is flying far away. The framework recognizes the QR code and decodes the position of a UAV by the corner points of the QR code. Further, we use the Kalman filter to fuse the position data decoded from the QR code with those from the inertia measurement unit sensor. Then, the position data are used for UAV landing with a developed hierarchical landing strategy. To verify the effectiveness of the proposed system, we performed experiments in different environments under various light conditions. The experimental results demonstrate that the proposed system can achieve UAV landing with high accuracy, strong adaptability, and robustness. In addition, it can achieve accurate landing in different operating environments without external real-time kinematic global positioning system (RTK-GPS) signals, and the average landing error is 11.5 cm, which is similar to the landing error when using RTK-GPS signals as the ground truth.

3D Hybrid Localization Algorithm for Mitigating NLOS Effects in Flying Ad Hoc Networks

Positions of unmanned aerial vehicles (UAVs) are typically obtained using the global positioning system (GPS). However, in GPS-denied or GPS-degraded environments, ad hoc networks with flying sensor nodes are used for UAV localization. In this study, we propose a novel three-dimensional (3D) localization algorithm for UAVs in flying ad hoc sensor networks. Interacting multiple model probability data association and finite impulse response filters are integrated in our hybrid localization algorithm. The non-line-of-sight condition can be overcome using the proposed algorithm, which is demonstrated through 3D localization simulations based on flying ad hoc networks.

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

Completion Time Minimization for Data Collection in a UAV-enabled IoT Network: A Deep Reinforcement Learning Approach

In this paper, we study the completion time minimization in an unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) network, where the UAV tries to collect all the data generated by the ground IoT devices for further processing. To simplify the analysis, the continuous time horizon is discretized into several time slots. The duration of each time slot is set to be less than the pre-defined threshold such that the UAV's location can be considered as unchanged during each time slot. In our work, we aim to minimize the completion time of the UAV by optimizing the association scheme of the IoT devices, the location (i.e., the trajectory) and velocity of the UAV at each time slot. However, the formulated problem is challenging to solve by traditional optimization methods considering the unknown number of time slots (which leads to the unknown number of decision variables) and non-convex functions. We thus reformulate it as a Markov decision process (MDP) and propose a deep deterministic policy gradient (DDPG)-based method to efficiently solve it. The DDPG-based algorithm uses deep function approximators instead of finding the action that maximizes the state-action value, and is therefore well suited to solve high-dimensional, continuous control problems. Extensive numerical results are presented to validate the effectiveness of our proposed algorithm.

PARouting: Prediction-supported adaptive routing protocol for FANETs with deep reinforcement learning

Flying Ad-hoc Networks (FANETs) are becoming increasingly popular for various applications. Effective routing protocols for FANETs are essential yet challenging due to the high dynamic nature of Unmanned Aerial Vehicles (UAVs). Most existing routing protocols require the periodic broadcast of Hello packets to maintain neighbor tables that store the locations of neighbors, mobility patterns, etc. However, the frequent exchange of Hello packets leads to a large routing overhead in FANETs. This paper proposes PARouting, a prediction-supported adaptive routing protocol with Deep Reinforcement Learning, which introduces a novel UAV mobility prediction algorithm using Deep Learning (DL-UMP) to estimate the locations of UAVs. Based on DL-UMP, we design an adaptive Hello packet mechanism to realize on-demand broadcasting of Hello packets, which reduces routing overhead. The routing process is formulated as a Partially Observable Markov Decision Process, and a new Q-network structure is proposed to select the optimal next hop. Simulation results confirm the accuracy of the DL-UMP and show that PARouting outperforms benchmark routing protocols in terms of packet delivery rate, end-to-end delay, and routing overhead.

Throughput Maximization for UAV-Enabled Integrated Periodic Sensing and Communication

Driven by unmanned aerial vehicle (UAV)’s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem with location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

Intra-UAV Swarm Connectivity in Unstable Environment

Swarms of unmanned aerial vehicles (UAVs) are being explored as viable options for a variety of missions. However, extremely dynamic and rapidly changing environmental conditions such as wind prevent UAVs from maintaining a minimum safe distance and cause them to lose Connectivity. Furthermore, the UAVs near the swarm's edge will be more sensitive to connectivity failure. This study presents a thorough examination of the impact of wind on the connectivity of a large-scale swarm. The correlation between the number of UAVs needed to sustain swarm connectivity and characteristics like UAV speed, mass, wind speed, and drag force is also explored. From the results, the number of UAVs that do not maintain the minimum safe distance are seen to increase with increasing absolute value difference between the UAV and wind speed. Moreover, the number of critical UAVs is seen to decrease as the speed of UAVs increases relative to the mean wind speed for the same percentage of change in distance relative to the expected distance to be covered with the flight speed. Extending on these findings, a novel algorithm for updating UAV locations in a swarm in a constantly changing environment is also provided in order to maintain intra-UAV connectivity between individual UAV's. In this regard, a thorough examination of the impact of flying and maneuvering tactics selection is offered. As a result of comprehensive study, the proposed approach for updating UAV locations in uncertain conditions obtained a reduction in average outage probability of around 35%, 10%, and 40% using three distinct approaches, respectively, in contrast to earlier efforts. In addition, as compared to previous techniques, the proposed algorithm produced a smoother flight path with less jitters.

Advances in UAV-Assisted Localization: Joint Source and UAV Parameter Estimation

This paper presents efficient estimators to address the problem of source localization aided by unmanned aerial vehicles (UAVs) with unknown location parameters. Leveraging semidefinite relaxation technique and successive weighted least squares estimation, the source and UAV parameters are simultaneously estimated in both mixed line-of-sight/non-line-of-sight (NLOS) and NLOS scenarios. The proposed estimators are shown to approximately achieve the Cramer-Rao bound (CRB) under mild Gaussian noise conditions when the measurement noises are small compared to the associated ranges. Numerical simulations demonstrate that localization without prior UAV location information is still possible with a meter-level positioning and sub-meter per seconds velocity estimation accuracy.

Machine Learning Modeling of GPS Features with Applications to UAV Location Spoofing Detection and Classification

In this paper, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.