Altitude Of Unmanned Aerial Vehicle Research Articles

A More Efficient Algorithm for Small Target Detection in Unmanned Aerial Vehicles

Due to the relatively high shooting altitude of unmanned aerial vehicles (UAV), the captured images often contain a multitude of small‐scale targets. To solve the problems of small target scale, lack of semantic information, and high miss detection in drone target detection, in this paper we proposed a more effective unmanned aerial vehicle small target detection algorithm(MEU‐YOLOv5) based on YOLOv5s. Firstly, an efficient global contextual module is proposed to enhance the algorithm's performance in feature extraction while reducing the excessive loss of shallow features. Secondly, a small‐scale target detector is added to enhance the algorithm's detection capability for smaller targets. Lastly, a recursive multi‐level feature fusion path is introduced to better fuse the shallow and deep features of the images, reducing overfitting and improving the algorithm's generalizability and robustness. Experimental results demonstrated that compared to YOLOv5s, MEU‐YOLOv5 achieves a 7.4% improvement in mAP@0.5 and a 4.9% improvement in mAP@0.5:0.95. Additionally, the overall performance of this algorithm surpassed various algorithms in the YOLO series, including YOLOv3, YOLOv5l, YOLOv5m, and YOLOv8s. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Spectrally Efficient UAV Communications using Non-Orthogonal Multiple Access (NOMA)

Non-orthogonal multiple access (NOMA) scheme with its share of benefits such as spectral efficiency and simultaneous support of massive connectivity, is one of the keys enabling technologies for upcoming 5G. However, in the context of UAV-assisted communication, existing literature mainly focuses on conventional orthogonal multiple access (OMA) which limits the spectral efficiency and number of simultaneous connections. Existing technologies are using LTE that only can serve 1 user per resource block. The user capacity nowadays is increased, so the existing technologies should be improved to higher user capacity such as 4 users per resource block. This research project focuses on the design of NOMA scheme for multiple users aiming to maximize the system capacity and spectral efficiency of Unmanned Aerial Vehicle (UAV) hotspot with baseline model and to simultaneously support mobile users. The proposed NOMA multi-users model is then compared with baseline model of 2 users per resource block in NOMA and OMA. The proposed NOMA model increases spectral efficiency, higher throughput and improved user fairness. The project only focuses on downlink communication with stationary users. The parameters measured are Bit Error Rate, Sum Rate, and Spectral Efficiency that depend on power allocation, altitude of UAV, and distance between UAV and users. Based on the simulation results, NOMA which employs Fractional Transmit Power Allocation (FTPA) algorithm has better performance in terms of sum-rate and spectral efficiency compared to those of OMA by as much as 650%. Results are also presented for various NOMA power allocation algorithms.

RESULTS OF THE EXPERIMENT USING NAVIGATIONAL ANTENNAS WITH CONTROLLED RECEPTION PATTERN ANTENNA FOR UAVS IN AREAS OF ACTIVE SUPPRESSION BY ELECTRONIC WARFARE MEANS

The article describes the trends in development, tactics, composition, and construction of Russia's electronic warfare means, as well as the primary objectives of interference. It describes methods effectively employed on the battlefield to reduce the impact of enemy EW assets on the navigation data quality of UAVs (Unmanned Aerial Vehicles). The general concept of using CRPA (Controlled Reception Pattern Antenna) for military users' navigation is outlined.
 Experiments were conducted to assess the influence of EW means on one of the most popular CRPAs with four antenna elements in real conditions of active combat. Flights were carried out simultaneously by two UAVs, the first equipped with CRPA and the second integrated with a standard GNSS receiver. The flights were conducted under identical conditions, simultaneously conducting reconnaissance of the same targets. Data on the average height loss of navigation signal reception for UAVs were obtained.
 According to the experiment results, the integration of CRPA with four antenna elements can be effective for strike UAVs with weapon release systems that require precise positioning for target engagement, provided that the flight altitude of such UAVs does not exceed 150-250 meters in areas with minimal enemy EW interference. Otherwise, it is recommended to use CRPA with 8 or more elements or explore other promising technologies.
 Recommendations for integrating the aforementioned antennas are developed, and the accompanying issues associated with their application are discussed. The article also outlines options for expanding CRPA integration capabilities and ways to upgrade their software and hardware components.

Precision Landing of Unmanned Aerial Vehicle under Wind Disturbance Using Derivative Sliding Mode Nonlinear Disturbance Observer-Based Control Method

Unmanned aerial vehicles (UAVs) are extensively employed in civilian and military applications because of their excellent maneuverability. Achieving fully autonomous quadrotor flight and precision landing on a wireless charging station in the presence of wind disturbance has become a crucial research topic. This paper presents a composite control technique for UAV altitude and attitude tracking in harsh environments, i.e., wind disturbance. A composite controller was developed based on nonlinear disturbance observer (NDOB) control theory to allow the UAV to land in the presence of random external wind disturbances and ground effects. The NDOB estimated the unknown wind disturbance, and the estimation was fed into the derivative sliding mode nonlinear disturbance observer-based control (DSMNDOBC), allowing the UAV to perform autonomous precision landing. Two loop designs were applied: the inner loop for stabilization and the outer loop for altitude tracking. The quadrotor model dynamics and the proposed controller, DSMNDOBC, were simulated employing MATLAB/Simulink®, and the results were compared with the one obtained by the proportional derivative (PD) controller and the sliding mode controller (SMC). The simulation results indicated that the DSMNDOBC has superior altitude and attitude control compared to the PD and SMC controllers and better disturbance estimation and attenuation performance.

Autonomous flight strategy of an unmanned aerial vehicle with multimodal information for autonomous inspection of overhead transmission facilities

AbstractThis study proposes an innovative method for achieving autonomous flight to inspect overhead transmission facilities. The proposed method not only integrates multimodal information from novel sensors but also addresses three essential aspects to overcome the existing limitations in autonomous flights of an unmanned aerial vehicle (UAV). First, a novel deep neural network architecture titled the rotational bounding box with a multi‐level feature pyramid transformer is introduced for accurate object detection. Second, a safe autonomous method for the transmission tower approach is proposed by using multimodal information from an optical camera and 3D light detection and ranging. Third, a simple yet accurate control strategy is proposed for tracking transmission lines without necessitating gimbal control because it keeps the UAV's altitude in sync with that of the transmission lines. Systematic analyses conducted in both virtual and real‐world environments confirm the effectiveness of the proposed method. The proposed method not only enhances the performance of autonomous flight but also provides a safe operating platform for inspection personnel.

A novel dual-pooling attention module for UAV vehicle re-identification

Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

UAV-assisted finite block-length backscatter: Performance analysis and optimization

This paper introduces and investigates the performance of a system where an unmanned aerial vehicle (UAV) is utilized to assist terrestrial backscattering devices (BDs) in wireless energy charging and data transmission. The UAV task is data collection through the backscatter communication with finite block length. To assess performance, the study not only considers energy efficiency (EE) but also derives closed-form expressions for age-of-information (AoI). The block error rate (BLER) expression is approximated using Gaussian-Chebyshev quadrature and the first-order Riemann integral. Furthermore, the optimization problem is solved to maximize EE and minimize transmit power through a search algorithm. Both EE and spectral efficiency (SE) are investigate via the number of transmission bits. The analytical and simulation results show that the optimized EE and minimum transmit power of UAV can be obtained by selecting an appropriate packet length and UAV altitude. Besides, the Rician factor K also has a significant impact on BLER of the considered system. Finally, the study points out that the system performance is strongly influenced by factors such as the utilized bandwidth, the specific operating environments, and the elevation angle of UAV.

Outage analysis of aerial semi-grant-free NOMA systems

In this paper, we analyze the outage performance of Unmanned Aerial Vehicles (UAVs)-enabled downlink Non-Orthogonal Multiple Access (NOMA) communication systems with the Semi-Grant-Free (SGF) transmission scheme. A UAV provides coverage services for a Grant-Based (GB) user and one Grant-Free (GF) user is allowed to utilize the same channel resource opportunistically. The analytical expressions for the exact and asymptotic Outage Probability (OP) of the GF user are derived. The results demonstrate that no-zero diversity order can be achieved only under stringent conditions on users' quality of service requirements. Subsequently, an efficient Dynamic Power Allocation (DPA) scheme is proposed to relax such data rate constraints. The analytical expressions for the exact and asymptotic OP of the GF user with the DPA scheme are derived. Finally, Monte Carlo simulation results are presented to validate the correctness of the derived analytical expressions and demonstrate the effects of the UAV's location and altitude on the OP of the GF user.

Cellular localizability of unmanned aerial vehicles

To enable pervasive applications of cellular-connected unmanned aerial vehicles (UAVs), localization plays a key role. The successful reception of localization signals from multiple base stations (BSs) is the first step to localize targets, which is called cellular localizability. In this paper, we propose an analytical framework to characterize the B-localizability of UAVs, which is defined as the probability of successfully receiving localization signals above a certain signal-to-interference plus noise ratio (SINR) level from at least B ground BSs. Our framework considers UAV-related system parameters in a three-dimensional environment and provides a comprehensive insight into factors affecting localizability such as distance distributions, path loss, interference, and received SINR. We perform simulation studies to explore the relationship between localizability and the number of participating BSs, SINR requirements of the received localization signals, air-to-ground channel characteristics, and network coordination. We also formulate an optimization problem to maximize localizability and investigate the effects of UAV altitude in different scenarios. Our study reveals that in an urban macro environment, the effectiveness of cellular network-based localization increases with altitude, with localizability reaching 100% above 60 meters. This finding indicates that utilizing cellular networks for UAV localization is a viable option.

Micro-Doppler Signature Detection and Recognition of UAVs Based on OMP Algorithm.

With the proliferation of unmanned aerial vehicles (UAVs) in both commercial and military use, the public is paying increasing attention to UAV identification and regulation. The micro-Doppler characteristics of a UAV can reflect its structure and motion information, which provides an important reference for UAV recognition. The low flight altitude and small radar cross-section (RCS) of UAVs make the cancellation of strong ground clutter become a key problem in extracting the weak micro-Doppler signals. In this paper, a clutter suppression method based on an orthogonal matching pursuit (OMP) algorithm is proposed, which is used to process echo signals obtained by a linear frequency modulated continuous wave (LFMCW) radar. The focus of this method is on the idea of sparse representation, which establishes a complete set of environmental clutter dictionaries to effectively suppress clutter in the received echo signals of a hovering UAV. The processed signals are analyzed in the time-frequency domain. According to the flicker phenomenon of UAV rotor blades and related micro-Doppler characteristics, the feature parameters of unknown UAVs can be estimated. Compared with traditional signal processing methods, the method based on OMP algorithm shows advantages in having a low signal-to-noise ratio (-10 dB). Field experiments indicate that this approach can effectively reduce clutter power (-15 dB) and successfully extract micro-Doppler signals for identifying different UAVs.

Low-Altitude UAV Air-to-Ground Multilink Channel Modeling and Analysis at 2.4 and 5.9 GHz

Due to low cost and high operability, unmanned aerial vehicle(UAV) becomes a key component in future intelligent transportation system(ITS) for various use cases. Air-to-ground(A2G) communication is a fundamental technology for integration of UAV in the ITS, where accurate analysis and modeling of UAV A2G wireless channel are of great importance for communication algorithm development. In this paper, we present a comprehensive analysis and modeling of low altitude UAV A2G channel at 2.4 and 5.9 GHz based on multi-link channel measurement campaign. Based on the measured data, the value of path loss as a function of the elevation angle and distance is proposed. Further, the shadow fading(SF) is analyzed for different frequency bands and the spatial correlation is evaluated. Particularly, it is found that the influence of UAV altitude on shadowing is significant and should be considered when developing A2G communication system.

Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

Assessing narrow brown leaf spot severity and fungicide efficacy in rice using low altitude UAV imaging

The utilization of unmanned aerial vehicle (UAV)-based imaging systems offers precise detection of plant diseases and aids in decision-making regarding fungicide applications to optimize disease control. This study employed a twin-lens multispectral camera mounted on a low-altitude UAV to capture imagery data from rice field plots that were treated with different fungicides. The objectives of this study were to assess the severity of narrow brown leaf spot (NBLS) caused by Cercospora janseana and to evaluate the efficacy of fungicide control. Eighteen color features and vegetation indices were extracted from RGB images, while nine color features and vegetation indices were extracted from multispectral images. Through correlation analysis, four spectral features, namely Lab-a, ExGR, VDVI, and g, were found to exhibit high correlations with disease severity. Specifically, RGB imagery had greater correlation coefficients (exceeding 0.95) for both ExGR and Lab-a features compared to multispectral imagery. A multifeature inversion modeling approach was employed, using support vector regression with the top four spectral features to predict NBLS severity. The results indicated R2 values were above 0.93 for all support vector regressions. Furthermore, the efficacies of ten different fungicide treatments were evaluated, with UAV imaging consistently aligning with ground truth rating data in terms of efficacy ranking. These results demonstrate the potential of UAV imagery for use as a valuable tool for NBLS detection and assessing fungicide efficacy, offering significant benefits in the management of NBLS, which is a globally important disease in rice.

Performance analysis of UAV assisted triple-hop mixed FSO/RF/UWOC system with altitude optimization

Abstract In this paper, we propose a unmanned aerial vehicle (UAV) assisted triple hop mixed FSO/RF/UWOC system to establish a communication between distant users. In the proposed system, the source sends information signal towards a hovering UAV such as drone acting as a first intermediate node via FSO link modelled by Gamma-Gamma distribution. The drone further transmits the received signal to the second intermediate node located on a floating vehicle (FV) on the ocean surface via a RF link modelled by the Nakagami-m fading distribution and from where it is finally forwarded towards the deep ocean destination via visible light channel (VLC) link which is modelled by Exponential Generalized Gamma (EGG) fading distribution. Both the relay nodes act as decode and forward relays. For this asymmetric triple hop mixed communication system, we obtain a new exact closed form analytical expression for the outage probability and also study its behaviour at high signal to noise ratio (SNR) regime. Additionally, the UAV altitude optimization is also carried out for the proposed system to find the optimal altitude of the UAV and the optimal elevation angle to maximize the system’s performance. The numerical simulation results illustrate the accuracy of the proposed system model and present the influence of the various link parameters such as atmospheric path loss, turbulence, pointing errors, angle-of-arrival fluctuations, multipath fading parameter, water turbulence, and scintillation on the performance of the proposed system.

Outage and Throughput Analysis of UAV-Assisted NOMA Relay Systems With Indoor and Outdoor Users

Non-orthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the fifth generation (5G) and beyond networks. On the other hand, the transition environment from outdoor to indoor is an important problem in practical wireless communication. In this paper, we mathematically study a multi-antenna UAV-assisted communication system where a multi-antenna base station (BS) communicates with indoor and outdoor users through a multiantenna UAV using the power-domain NOMA technique. The metric used to evaluate the system performance are outage probability (OP) and throughput of indoor and outdoor users under different communication urban environments. Moreover, the impacts of the UAV's altitude and location and the light-of-sight (LoS) probability on system performance are also considered. We give the exact and asymptotic closed-form OP expressions and then optimize the transmission rate to maximize the throughput of each user and the overall system throughput. Numerical results show that better system performance can be achieved by controlling the altitudes and location of the UAV, selecting an appropriate transmission rate, or equipping BS and UAV with more antennas. Monte-Carlo simulation results are provided to demonstrate the performance of the investigated system and the accuracy of the developed analytical results.

Quantitative estimation of organ-scale phenotypic parameters of field crops through 3D modeling using extremely low altitude UAV images

Plant phenotypic parameters provide key information in modern crop breeding. However, the rapid and accurate estimation of organ-scale phenotypic parameters remains a challenge. In this context, the present study proposed a novel methodology for the automatic quantification of the organ-scale parameters of field crops using the unmanned aerial vehicle (UAV) platform. First, a lightweight UAV was employed to capture the multi-view and high-resolution image sequences of field crops at an extremely-low flight altitude. Subsequently, based on these image sequences, point cloud reconstruction of the canopy was conducted. Next, the geometrical model of individual leaves was reconstructed using the modified Crust algorithm and optimized by abnormal facet elimination and leaf surface repair. Finally, individual leaf phenotypic parameters were calculated based on the reconstructed geometrical models. The method was evaluated by comparing the calculated parameters with actual measurements. The calculated values for leaf length, maximum leaf width, and leaf area were in good agreements with the measured values (maize: R2 > 0.97 for all parameters, RMSE for length, width, and leaf area was 2.6 cm, 0.4 cm, and 33.2 cm2, respectively; soybean: R2 > 0.85 for all parameters, RMSE for the counterparts was 0.3 cm, 0.5 cm, and 2.3 cm2, respectively; tobacco: R2 > 0.89 for all parameters, RMSE for the counterparts was 4.1 cm, 1.4 cm, and 42.6 cm2, respectively). The methodology based on extremely-low altitude UAV images has promising prospects in the crop breeding program for the automatic acquisition of fine organ-scale parameters with high efficiency.

Antenna Selection and Device Grouping for Spectrum-Efficient UAV-Assisted IoT Systems

Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) systems have been implemented for over a decade, from transportation to military surveillance, and is proven worthy of integration in the next generation of wireless protocols. Though UAVs have immense potential, they have major drawbacks when it comes to real-world implementation, such as energy capacity, loss of signal quality, and spectrum limitations. To overcome these challenges, integration of UAVs with spectrum-efficient techniques including cognitive radio (CR) and non-orthogonal multiple access (NOMA) has been proposed. In this paper, we incorporate transmit-antenna selection (TAS) into an underlay CRNOMA network, which provides additional benefits through employing multiple-antenna-selection approach at the UAV with the goal of better serving the ground NOMA devices. The links associated with the multi-antenna UAV are theoretically assumed to experience Nakagami-m fading distribution. We also emphasize the degraded performance caused by imperfect successive interference cancellation (SIC) when decoding signals at the ground NOMA devices. The closed-form expressions for the proposed model are derived to evaluate two main performance metrics, namely the outage probability and the ergodic capacity. Monte-Carlo simulations are performed to analyze the performance of the system in different scenarios. We observe that the power allocation factors for the devices in a group and the altitude of UAV have a noticeable impact on the performance of the system. Furthermore, the increase in the number of antennas at the UAV can complement these effects and further improve the system performance.

Efficient Data Collection in UAV-Assisted Cluster-Based Wireless Sensor Networks for 3D Environment: Optimization Study

Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.

Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning

Aiming at the unmanned aerial vehicle (UAV)-assisted federated learning wireless-network scenario, and considering the influence of the UAV altitude on the coverage area, we propose a joint optimization algorithm of UAV placement, computation and communication resources. Considering the energy efficiency and federated learning performance, we defined the cost function of the system. Under the constraint of the total delay of federated learning completion, we formulated an optimization problem of minimizing the cost function to achieve the balance between the total energy consumption of users and the federated learning performance. Since the formulated optimization problem is a non-convex problem, in order to solve this problem, we decomposed it into three optimization subproblems: UAV horizontal placement, local accuracy and computation and communication resources. We used successive convex approximation (SCA), the Dinkelbach Method, the Bisection method and KKT condition, respectively, to solve the three subproblems, and finally obtain the optimal solutions through iteration of the three subproblems. Simulation results show that compared with the federated learning scenario under fixed UAV altitude, our proposed algorithm not only guarantees the learning performance, but also reduces more users’ total energy consumption.

Disturbances due to unmanned aerial vehicles (UAVs) on harbor seal (Phoca vitulina) colonies: Recommendations on best practices

AbstractThe increase in the use of unmanned aerial vehicles (UAVs) in marine mammal research has raised concerns, as its potential impacts remain largely unknown. To assess the effects of disturbances on harbor seals by a DJI Phantom 4 Pro, behavioral responses were recorded before, during, and after UAV approaches at two colonies. GLMMs were applied to assess proportions of seals displaying vigilance, resting, locomotion, and flushing behaviors. The proportion of time that selected individuals spent vigilant, and resting was also analyzed. Responses were evaluated as a function of UAV altitude, time of day, study site, group size, and environmental covariates. UAV presence triggered a significant increase in the proportion of seals vigilant and a decrease in resting. Greater reactions were observed at lower flying altitudes between 20 m and 10 m, although due to our study design, seals could have been reacting to lower flying altitudes and/or longer exposure times. Sound levels at different flying altitudes were investigated to aid interpretation of our results and to provide reference levels for future studies. Based on the disturbance thresholds identified, we devised recommendations on best practices to be implemented in future research on harbor seals using UAV techniques.