Classification Of Unmanned Aerial Vehicles Research Articles

About the Organization of Regional Situational Centers of the Intellectual System “Control_Tee” with the Use of Uavs

The basics of the principles of creation and filling of the technopark of unmanned aerial vehicles (UAV) are offered. The business process of UAV registration in the technopark of the situation center robots is described. The use of attributes (tags) to the UAV will increase the efficiency of a suitable device selection with a complete set that meets the task. It is proposed to organize regional situational centers, which will coordinate the work of state systems, urban life support systems, private services on the one hand and UAV on the other as executors. At this time, principles and methods of automatic selection of UAVs are being developed for the prompt solution of the given task. The system should be based on the classification of the UAV during registration and on the basis of the UAV compliance function to the specific task, key features of the UAV, the time required to complete the task, the distance to the task and the policy to involve the UAV in such tasks to select those devices, who can quickly perform tasks. The function of classification of a specific UAV by tasks is offered. The function takes into account the configuration of the UAV. For each task, experts and automation select weights that indicate how large the impact of certain typical modules to perform a particular task, and for each UAV in the database stores information about whether the UAV takes into account a certain characteristic, or there is a corresponding module. To update the approach to professional training today, one of the main tasks is to create interactive mock-up training systems in real physical space. The principles of building a basic laboratory stand, its equipment and the control system of a mobile modular robot, which helps to master the basic functions of a UAV or mobile operation of a ground base, are under development.

A cost-effective method to monitor vegetation changes in steppes ecosystems: A case study on remote sensing of fire and infrastructure effects in eastern Mongolia

Land degradation is a major environmental and social issue in temperate steppes. It is commonly determined from vegetation cover using remote sensing techniques. Steppes in eastern Mongolia are subject to resource extraction activities, such as mining and oil extraction, which affect land degradation. Recent technological progress in remote sensing has facilitated the acquirement of high-resolution data by, for example, the CubeSat satellite or unmanned aerial vehicles (UAV), providing data for detailed maps of vegetation cover and plant functional groups (PFGs). Traditional methods for monitoring vegetation cover often face typical scale issues, such as the upscaling of vegetation parameters if plot-scale field measurements are integrated to satellite data. Here, we studied the spatial distribution of PFG using machine learning and a combination of field measurements, UAV imagery (spatial resolution: 2 cm), and PlanetScope multi-temporal imagery. We provide two products at two spatial resolutions: one for UAV data, which is restricted to comparatively small areas around field measurements, and one for PlanetScope, which covers large parts of northeastern Mongolia. The results showed that the overall accuracies of UAV classification were 91–95%, whereas those of PlanetScope were 78–95%. In integrating the classified UAV data to the PlaneScope data, our proposed model minimized the scale issue that often impedes classification. Importantly, our findings revealed that the ecological effects of dirt road and railroad extended up to 60–120 m into the adjacent, otherwise less degraded steppe vegetation. A comparison between burned and unburned areas in different years indicates that wildfires affect the composition of PFG in reducing the fractional cover of graminoids and forbs, and that increasing cover of bare ground leads to a distinct and patchy mosaic of different vegetation types.

RF-based low-SNR classification of UAVs using convolutional neural networks

Unmanned Aerial Vehicles (UAVs), or drones, which can be considered as a coverage extender for Internet of Everything (IoE), have drawn high attention recently. The proliferation of drones will raise privacy and security concerns in public. This paper investigates the problem of classification of drones from Radio Frequency (RF) fingerprints at the low Signal-to-Noise Ratio (SNR) regime. We use Convolutional Neural Networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.

Resource management in UAV-assisted wireless networks: An optimization perspective

Wireless networks are expected to provide connectivity to an increasing number of users with heterogeneous requirements. Future wireless networks will integrate aerial and terrestrial networks to provide massive (a large number of users) connectivity. Unmanned aerial vehicles (UAV) can be deployed on-demand to provide connectivity in aerial networks. UAV-assisted wireless networks offer a broad range of applications in an overload situation, broadcasting and advertisement, public safety, disaster management, and many more. However, UAV-assisted wireless networks must use their resources efficiently to maintain key performance indicators. The challenges for the successful deployment of UAV-assisted wireless networks include spectrum efficiency, energy consumption, deployment time, backhaul, and cost of deployment. This paper provides a comprehensive survey of resource management in UAV-assisted wireless networks from an optimization viewpoint. We present the classification of UAVs, their benefits, and applications to highlight UAV-assisted wireless networks’ significance. We then provide a detailed discussion on resource management with metrics including placement of UAVs, UAV trajectory, backhaul, path planning, charging, spectrum, and data offloading. Moreover, different constraints, optimization types, and solutions tailored to support UAV-assisted wireless networks are discussed. Finally, we provide future research directions to address the challenges for the successful deployment of UAV-assisted wireless networks.

Architecture, Classification, and Applications of Contemporary Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAV) have been gaining significant attention in recent times as they are becoming increasingly accessible and easier to use. This article serves as an introduction to UAV systems’ architecture, classification, and applications to help researchers and practitioners starting in this field get adequate information to understand the current state of UAV technologies. The article starts by inspecting the UAVs’ body configuration styles and explains the physical components and sensors that are necessary to operate and fly a UAV system. The article also provides a comparison of several components for state-of-the-art UAVs. The article further discusses different propulsion methods and various payloads that could be mounted on the UAV. The article then explores the classification of UAVs followed by the application of UAVs in different domains, such as recreational, commercial, and military. Finally, the article provides a discussion of futuristic technologies and applications of UAVs along with their associated challenges.

RF Fingerprinting Unmanned Aerial Vehicles With Non-Standard Transmitter Waveforms

The universal availability of unmanned aerial vehicles (UAVs) has resulted in many applications where the same make/model can be deployed by multiple parties. Thus, identifying a specific UAV in a given swarm, in a manner that cannot be spoofed by software methods, becomes important. We propose RF fingerprinting for this purpose, where a neural network learns subtle imperfections present in the transmitted waveform. For UAVs, the constant hovering motion raises a key challenge, which remains a fundamental problem in previous works on RF fingerprinting: Since the wireless channel changes constantly, the network trained with a previously collected dataset performs poorly on the test data. The main contribution of this paper is to address this problem by: (i) proposing a multi-classifier scheme with a two-step score-based aggregation method, (ii) using RF data augmentation to increase neural network robustness to hovering-induced variations, and (iii) extending the multi-classifier scheme for detecting a new UAV, not seen earlier during training. Importantly, our approach permits RF fingerprinting on manufacturer-proprietary waveforms that cannot be decoded or altered by the end-user. Results reveal a near two-fold accuracy in UAV classification through our multi-classifier method over the single-classifier case, with an overall accuracy of 95% when tested with data under unseen channel. Our multi-classifier scheme also improves new UAV detection accuracy to a near perfect 99%, up from 68% for a single neural network approach.

Restriction And Classification Of Unmanned Aerial Vehicles In War According To The Principles Of International Law

This research aims to find out how international law exists in the assessment and limits the company of Unmanned Aerial Vehicles (UAVs) in war from international legal principles and provides an overview of the classification of UAVs as weapons based on existing regulations. By regulations and international legal products that are the limiting parameters of UAVs such as St. Petersburg 1868, The Hague Convention 1907 and also pay attention to the foresight regarding the classification of UAVs themselves. The research method used is the juridical normative legal research method. The problem in this article is how to limit the use of UAVs in the war based on the principles of international law and knowing how to classify UAV classes as weapons in battle according to international law. Based on the research results on the use and classification of UAVs in the world of firearms, it is still far from clear. It has not been able to provide "safety" parameters clearly and unequivocally in international legal products. The use of UAVs as weapons is still relatively new. Researchers categorize UAVs now better known as the AWS category. The use of which will be limited by the principles of international law.

Presretanje i detekcija besposadnih platformi koristeći RF bazu podataka

The usage of Unmanned Aerial Vehicles (UAVs) is accessible for different applications to a wide range of users. However, this wide range of users raises a great concern about the threat (passive or active threats) of malicious actors who can use UAVs for criminal activities. The detection of UAVs is considered to be the first step in the process of UAVs countering (c-UAV). Nowadays, the c-UAV applications offer systems that include different sensors such as electro-optical, thermal, acoustic, radar and radio frequency sensors. Information gathered by these sensors can be fused in order to increase the reliability of threat's detection, classification and identification. It is necessary to have datasets from the different sensors in order to develop methods and algorithms for detection and classification of UAVs. This paper presents a dataset of communication signals between the drone and the control station that is used in the process of detection and classification.

Detection and Classification of UAVs Using RF Fingerprints in the Presence of Wi-Fi and Bluetooth Interference

This paper investigates the problem of detection and classification of unmanned aerial vehicles (UAVs) in the presence of wireless interference signals using a passive radio frequency (RF) surveillance system. The system uses a multistage detector to distinguish signals transmitted by a UAV controller from the background noise and interference signals. First, RF signals from any source are detected using a Markov models-based naive Bayes decision mechanism. When the receiver operates at a signal-to-noise ratio (SNR) of 10 dB, and the threshold, which defines the states of the models, is set at a level 3.5 times the standard deviation of the preprocessed noise data, a detection accuracy of 99.8% with a false alarm rate of 2.8% is achieved. Second, signals from Wi-Fi and Bluetooth emitters, if present, are detected based on the bandwidth and modulation features of the detected RF signal. Once the input signal is identified as a UAV controller signal, it is classified using machine learning (ML) techniques. Fifteen statistical features extracted from the energy transients of the UAV controller signals are fed to neighborhood component analysis (NCA), and the three most significant features are selected. The performance of the NCA and five different ML classifiers are studied for 15 different types of UAV controllers. A classification accuracy of 98.13% is achieved by k-nearest neighbor classifier at 25 dB SNR. Classification performance is also investigated at different SNR levels and for a set of 17 UAV controllers which includes two pairs from the same UAV controller models.

Щодо перспектив використання безпілотних пасажирських літальних апаратів з електричними рушійними установками в Україні

The article explores the issue of institutional provision for the employment of unmanned passenger aerial vehicles with electric propulsion (urban electric aerial vehicles) in Ukraine. The use of such vehicles is considered a promising direction in the development of urban air transport, able to unload traffic bottlenecks of the transport system in megacities. Among the main factors that favorably distinguish urban electric aerial vehicles from traditional air transport are: safety, economic efficiency, quietness, environmental friendliness and low requirements for related infrastructure. Preparations for large-scale use of urban electric aerial vehicles are currently being carried out in Germany, the USA, South Korea and the city of Dubai. The analysis of the advantages and possibilities of using urban electric aerial vehicles is provided; the history of development and the current status of this transport type is carried out. Despite the fact that the first samples of urban electric aerial vehicles are only being tested, there is already a significant competition in this market. Among the main developers of this class of aircraft one can single out the companies EHang (PRC) and Lilium GmbH (Germany). The article reviewes legislative requirements for the use of unmanned aerial vehicles with electric propulsion in certain countries of the world. Borrowing this experience can be useful for Ukraine due to the lack of terminological definition of such a class of vehicle in the natonal legislation; uncertainty of the conditions of their operation and safety measures; lacunas in the existing classification of unmanned aerial vehicles, which does not provide for the existence of programmable aerial vehicle, the control of which does not require a special control station located outside the vehicle, or vehicles that carry out programmable flight. In order to eliminate existing lacunas in Ukrainian legislation, measures have been developed, the implementation of which will allow the use of unmanned passenger aerial vehicles with electric propulsion and will contribute to the further development of air transport in Ukraine.

Routing in Flying Ad Hoc Networks: A Comprehensive Survey

The use of unmanned aerial vehicles (UAVs) is attracting considerable interest in academic research, commercial, and military applications. Multi-UAV systems organized in an ad hoc fashion called a flying ad hoc network (FANET) can cooperatively and collaboratively accomplish complex missions more efficiently compared to single large UAV systems. However, the unique features of FANETs such as high mobility, low node density, and high frequency of topology changes introduce challenges to the communication design, especially routing. Thus, the routing requirements of FANETs surpass those of MANETs or VANETs. In this paper, we present UAV classification, communication and application architectures, and an exhaustive survey of the existing routing protocols for flying ad hoc networks. Furthermore, we highlight the key features, strengths and weaknesses, and different mobility models used for the performance evaluation of the existing FANET routing protocols. More importantly, a proposed taxonomy and a review on the existing FANET routing protocols are presented. Finally, we highlight the existing challenges and open research issues.

АНАЛІЗ СУЧАСНОГО СТАНУ РОЗВИТКУ ВИСОТНИХ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

The development of unmanned aerial vehicles, at this time, is of great interest, both of the largest aircraft building companies and design enthusiasts, and among the total amount of development, the volume of high-altitude unmanned vehicles occupies one of the leading positions. In this regard, a very topical issue is the analysis of existing developments and the determination of the vector of future research in this direction. High-altitude unmanned vehicles have a wide range of functions, both in the field of military and civilian use. The work collected and analyzed statistical data of high-altitude unmanned aerial vehicles to determine advances in the design of high-altitude unmanned aerial vehicles (UAVs). The current classification of UAVs was considered, as a result of the analysis of statistical data, options for its expansion were proposed. The flight characteristics of high-altitude UAVs are described. There are charts and tables showing the place of highaltitude UAVs in their total number. Also, flight performance, aerodynamic schemes and engine type, which are the most rational for high-altitude unmanned aerial vehicles according to their purpose and class, are defined.

Hybrid energy systems in unmanned aerial vehicles

Purpose The presented research is carried out in reaction to the soaring costs of fuel and tight control over environmental issues such as carbon dioxide emissions and noise. The purpose of this paper is to study the feasibility of applying the environmental-friendly energy source in an unmanned aerial vehicles (UAVs) propulsion system. Design/methodology/approach Currently, the majority of UAVs are still powered by conventional combustion engines. An electric propulsion system is most commonly found in civilian micro and mini UAVs. The UAV classification is reviewed in this study. This paper focuses mainly on application of electric propulsion systems in UAVs. Investigated hybrid energy systems consist of fuel cells, Li-ion batteries, super-capacitors and photovoltaic (PV) modules. Current applications of fuel cell systems in UAVs are also presented. Findings The conducted research shows that hybridization allows for better energy management and operation of every energy source onboard the UAV within its limits. The hybrid energy system design should be created to maximize system efficiency without compromising the performance of the aircraft. Practical implications The presented study highlights the reduction of the energy consumption, necessary to perform the mission and maximizing of the endurance with simultaneous decrease in emissions and noise level. Originality/value The conducted research studies the feasibility of implementing the environmental-friendly hybrid electric propulsion systems in UAVs that offers high efficiency, reliability, controllability, lack of thermal and noise signature, thus, providing quiet and clean drive with low vibration levels. This paper highlights the main challenges and current research on fuel cell in aviation and draws attention to fuel cell – electric system modeling, hybridization and energy management.

A UAV classification system based on FMCW radar micro-Doppler signature analysis

Due to its small size, slow flying speed, and low flying altitude, classification of mini-sized unmanned aerial vehicles (UAVs) using a frequency-modulated continuous wave (FMCW) surveillance radar is a challenging task. This is because the FMCW radar echo signals are acquired at a short dwell time and thus contain limited information about targets. In this paper, we first analyze FMCW radar returns from various types of UAVs and non-UAV objects in terms of the micro-Doppler signature (m-DS) pattern. Based on the analysis results, we propose an effective and efficient UAV classification system using FMCW radar echo signals. The proposed system consists of five main parts namely, (i) burst selection, (ii) rule-based scan pruning, (iii) the empirical mode decomposition based m-DS analysis and features extraction, (iv) error counting minimization based class label estimation, and (v) scan-to-scan filtering. Our experimental results on physically measured FMCW radar echo signals from several types of UAVs and non-UAV objects show that the proposed system consistently outperforms a commercial-off-the-shelf UAV classification system in terms of the classification accuracy.

Surface Coverage Classification of UAV High Resolution Image Transmission Lines Corridor based on Total Convolution Neural Network

In recent years, UAV imagery has been applied more and more to the work of power line inspection. Surface coverage classification is a key step. Because of the size and amount of data of UAV high-resolution images, the accuracy requirements of traditional classification algorithms can no longer meet the practical needs. In this paper, a pixel level classification algorithm based on full convolution neural network is proposed. The total convolution neural network model reduces the error caused by other forms of deformation such as image translation, scaling, tilting and so on. In the application of UAV image, the total convolution neural network is used to classify the image, and the high dimensional information extracted from the coiling layer is used to learn the feature of the image fully, and the accuracy of the classification is improved. By comparing with the random forest algorithm, the advantages of the full convolution neural network in the classification of the surface coverage of the UAV transmission line corridor are verified, and the reference value is provided for the electric patrol line and the image classification of unmanned aerial vehicles.

INFORMATION CHARACTERISTICS OF SOUND RADIATION OF SMALL UNMANNED AERIAL VEHICLES

The results of comparison of experimentally obtained information characteristics of sound signals of two types of small unmanned aerial vehicles (UAVs) - a quadrocopter and a monoplane are given. Studies have shown that the energy spectra of the signals have a significant region of broadband noise and pronounced harmonic components with frequencies of multiple rotational speeds of the screw. Even with averaging of the spectral estimates for the ensemble of samples, harmonics with frequencies up to 8 ± 10 kHz are confidently observed. With a large difference in the modes of the quadrocopter engines, the spectral maxima are divided into several ones, which can be one of the most important factors for the UAV classification. The algorithm for operational recognition of UAV is described, which includes the formation of recognition features on the basis of spectral estimates for their orthogonal transformation and the decision rule based on the Dice criterion.

Information characteristics of sound emission from small unmanned aerial vehicles

The results of comparison of experimentally obtained information characteristics of sound signals of two types of small unmanned aerial vehicles (UAVs) - a quadrocopter and a monoplane are given. Studies have shown that the energy spectra of the signals have a significant region of broadband noise and pronounced harmonic components with frequencies of multiple rotational speeds of the screw. Even with averaging of the spectral estimates for the ensemble of samples, harmonics with frequencies up to 8 ± 10 kHz are confidently observed. With a large difference in the modes of the quadro copter engines, the spectral maxima are divided into severalones, which can be one of the most important factors for the UAV classification. The algorithm for operational recognition of UAV is described, which includes the formation of recognition features on the basis of spectral estimates for their orthogonal transformation and the decision rule based on the Dice criterion.

无人机遥感技术在景观生态学中的应用

PDF HTML阅读 XML下载 导出引用 引用提醒 无人机遥感技术在景观生态学中的应用 DOI: 10.5846/stxb201702270318 作者: 作者单位: 云南大学生态学与环境学院,云南大学生态学与环境学院,云南大学生态学与环境学院,云南大学生态学与环境学院,云南大学生态学与环境学院,云南大学生态学与环境学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划（2016YFC1201100）；国家自然科学基金项目（41361046） Applications of unmanned aerial vehicles remote sensing technology in landscape ecology Author: Affiliation: Institute of Ecology and Geobotany, Yunnan University,,,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:野外数据的获取是生态学研究的挑战之一，而通过遥感技术能够实现对地球表面的多面立体观测，获取丰富多样的空间信息数据，开展从微观到宏观不同尺度上的景观单元（包括物种、种群、群落、生态系统等）的空间关系研究。传统卫星遥感影像受空间和时间分辨率的限制，难以满足局域尺度或者时间序列上的景观空间生态学研究需求。无人机遥感技术为生态学研究的野外数据获取提供了一种新方法，以其灵活、高效、简便等特点弥补了传统卫星遥感的空间分辨率低、重访周期长、云雾影响等方面的不足，在景观空间生态学研究中受到越来越多的关注。简要介绍无人机类型及其搭载常见的传感器类型，分别从不同尺度的景观单元，即物种、种群、群落以及生态系统水平上探讨其应用进展，并指出当前无人机技术在景观生态学研究中存在的挑战与困难，同时展望了未来可能的研究热点，以期对今后无人机遥感技术在景观生态学领域的应用研究有所启发。 Abstract:Acquiring accurate, spatially explicit data is crucial for ecologists, particularly for long-term landscape and spatial ecological research. Remote sensing techniques have been widely used for collecting data on spatial and temporal ecological phenomena over the last decades. However, owing to the limitations of spatial and temporal resolution of satellite image data, they are sometimes hard to incorporate into regional or local temporal ecological studies. Unmanned aerial vehicles (UAV) remote sensing technology provides a new method for ecological field data acquisition. With its flexibility, efficiency, and convenience, UAV remote sensing technology overcomes the limitations of traditional satellite-based remote sensing in terms of resolution, revisit period, and cloud cover, which has been increasingly focused in studies on landscape ecology. The present study introduced UAV classification and sensor types, in addition to its applications in species, population, community, and ecosystem research. In addition, it pointed out the challenges involved in these applications. We look forward to the promising research area of UAV-based landscape ecology. 参考文献 相似文献 引证文献

CLASSIFICATION OF UNMANNED AERIAL VEHICLES AND ITS SIGNIFICANCE FOR CRIMINALISTIC PRACTICE

The paper clarifies the notion and develops generalized classification of unmanned aerial vehicles. There is substantiated the need to consider the proposed classification in the formulation of criminalistic requirements for tactical and technical data of unmanned aerial vehicles and systems, unmanned aviation complexes and characteristics of their targeted equipment, compliance with which will be most of all correspond to the satisfaction of urgent needs of the Field Criminalistics.

Classification of small UAVs and birds by micro-Doppler signatures

The popularity of small unmanned aerial vehicles (UAVs) is increasing. Therefore, the importance of security systems able to detect and classify them is increasing as well. In this paper, we propose a new approach for UAVs classification using continuous wave radar or high pulse repetition frequency (PRF) pulse radars. We consider all steps of processing required to make a decision out of the raw radar data. Before the classification, the micro-Doppler signature is filtered and aligned to compensate the Doppler shift caused by the target's body motion. Then, classification features are extracted from the micro-Doppler signature in order to represent information about class at a lower dimension space. Eigenpairs extracted from the correlation matrix of the signature are used as informative features for classification. The proposed approach is verified on real radar measurements collected with X-band radar. Planes, quadrocopter, helicopters, and stationary rotors as well as birds are considered for classification. Moreover, a possibility of distinguishing different number of rotors is considered. The obtained results show the effectiveness of the proposed approach. It provides the capability of correct classification with a probability of around 92%.