Спосіб декореляції сигналів пасивних завад в системі селекції рухомих цілей малої швидкості

Radar surveillance of unmanned aerial vehicles (UAVs) is actively developing area of scientific research. This article provides a review and analysis of publications in recent years devoted to the methods and radar systems of detection and recognition of classes and types of UAVs. It is noted that the most difficult targets for radar detection are low-sized, low-speed small UAVs (drones) flying at low and extremely low altitudes. If large and medium-sized UAVs can be detected by modern radar systems, then for the detection of small UAVs it is advisable to create specialized highly efficient, highly mobile, portable and inexpensive active UAV detection radars. The technical requirements for such radars are defined and recommendations for their implementation are provided. High-performance protection systems based on adaptive lattice filters are offered to protect UAV detection radars from noise jamming and passive interference. It is shown that the research on the methods of recognizing UAV classes and types is a development of the existing theory and technology of radar recognition of air targets.

Multi-antenna unmanned aerial vehicle (UAV) networks can improve throughput by spatial multiplexing and UAVs dominant line-of-sight (LOS) connections, which will intensify interference and exacerbate user fluctuation. This paper proposes user-centric interference cooperation in multi-antenna UAV networks, where multi-antenna beamforming and joint transmission are performed among cooperative UAVs, and derives a semi-closed expression of the ergodic rate using stochastic geometry. Specifically, a dynamic UAV group with adjustable radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${R}$</tex-math></inline-formula> is organized for each user that forms a 3D conical region to guarantee seamless service, which provides spatial multiplexing freedom via cooperative multi-antenna beamforming to achieve interference mitigation. In addition, both signal power and inter-cluster interference power are approximated by Gamma distributions with appropriate parameters experiencing generic Nakagami-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> fading in air-to-ground (AtG) channel. Furthermore, zero-forcing (ZF) beamforming is adopted with equal power allocation among beams, which is across the distributed set of UAVs within the cluster and considering different antenna loading. Numerical results show that the average ergodic rate can achieve 86.01% gain compared to the uncoordinated single-cell multi-antenna UAV network when UAV density is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${100\mathrm{/km^{2}}}$</tex-math></inline-formula> and UAV group radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${R}$</tex-math></inline-formula> is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${20\mathrm{m}}$</tex-math></inline-formula>. The optimal network parameters are investigated to maximize ergodic rate, thus providing guidelines for practical network configuration. For instance, when UAV density is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$50\mathrm{/km^{2}}$</tex-math></inline-formula> and loading factor is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K/M=2/16$</tex-math></inline-formula>, the optimal deployment altitude for UAVs is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ h=20m$</tex-math></inline-formula>, which can achieve a rate gain of 144% compared to the case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h=0\mathrm{m}$</tex-math></inline-formula>.

As a quite interesting subject, there are an increasing number of researches about UAV (Unmanned Aerial Vehicle) in the literature.These robots are quite suitable to dull, dirty and dangerous missions (e.g., over flights at low altitude, especially at night, and longrunning operations, which exposes the crew to extreme fatigue).Thus, an important application of these vehicles is the search operations involving multiple UAVs.However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring.This research topic is a multidisciplinary area of study in its beginning.Several issues can be studied related to cooperative UAV for search operations, such as centralized versus decentralized control, path planning for cooperative flies, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied for UAV cooperation and the application itself (e.g., patrolling, remote sensing, search and rescue operations supporting, etc.).This paper proposes a model that combines path planning algorithms, search patterns and multiagent coordination techniques to obtain a cooperative UAV model.Different path planning algorithms were studied aiming to get the most suitable to these kinds of operations, and the conclusions are presented.In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination.The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly.Comparing two cooperative UAVs with two non-cooperative UAVs, simulations of previous work showed an average increase in efficiency by about 100%.Besides, another aspect was simulated and evaluated: the impact of the density of the initial sweep.The computational simulations show a high potential of reducing even more the time of search.Furthermore, it is worth noting that the usefulness of cooperative UAVs is not only the increase in efficiency, but also the reduction of costs and risks for the crew.

Synthetic Aperture Radar (SAR) systems deployed on Unmanned Aerial Vehicles (UAVs) are emerging as a highly promising solution for a wide range of future remote sensing and monitoring applications. The integration of SAR’s well-established capabilities for high-resolution imaging and change detection with the flexibility, maneuverability, and cost-effectiveness of modern UAV platforms unlocks significant potential for innovative sensing missions. However, the practical implementation of SAR on UAVs presents considerable challenges, primarily due to the inherent limitations in UAV flight stability and precise navigation, which degrade image quality. As a result, robust autofocusing techniques are essential to correct motion-induced phase errors and ensure coherent image reconstruction. While numerous autofocusing methods have been proposed in the literature, most are designed under assumptions that do not hold for low-flying UAV platforms. Specifically, conventional algorithms often overlook the strong dependence of the SAR imaging geometry on both azimuth and range positions of the targets, which becomes pronounced at low altitudes. In this study, we introduce a novel autofocusing approach termed Geometry-Aware Entropy Minimization (GAME). The proposed algorithm reconstructs three-dimensional positioning errors by explicitly modeling the spatially variant acquisition geometry along the UAV trajectory. This geometry-aware formulation enables accurate autofocus correction under the challenging conditions associated with low-altitude UAV SAR acquisitions. The effectiveness of the GAME algorithm is demonstrated using real X-band SAR data collected by an octacopter platform, confirming its applicability and performance in practical scenarios.

Introduction. The operation of a radar system of automatic range tracking (ATR), equipped with a moving target selector (MTS) for the purpose of increasing assessment accuracy, under conditions of intensive passive interference in the form of reflections from the Earth's surface is considered. The level of such passive interference can significantly exceed useful signals. Range tracking by the ATR system is carried out by a pair of tracking half-strobes, which receive weakly correlated signals from different parts of the Earth's surface during each probing of the space. This reduces the efficiency of the MTS system and increases the error of range measurement. Aim. To study the correlation properties of passive interference in the modes of search and automatic tracking of moving targets by the ATR system depending on probing pulse duration and target velocity. To determine the efficiency of the MTS system as part of the ATR system when exposed to correlated interference. To evaluate the effect of probing pulse duration and target velocity on the passive interference suppression coefficient and range discriminator operation when the ATR system operates in the search and automatic tracking modes. Materials and methods. The method of correlation analysis of statistical properties of signals and devices for their processing was used. Results. We derived an expression for the correlation function of signal fluctuations reflected by a homogeneous Earth's surface, taking into account probing pulse duration and target velocity as applied to pulse Doppler radars. The results of calculating the passive interference suppression coefficient by means of the MTS as part of an interference-proof range discriminator for various values of probing pulse duration and target velocity are presented. It is shown that a decrease in probing pulse duration, as well as an increase in target velocity, leads to a significant decrease in the passive interference suppression coefficient and an increase in the dispersion of the interference signal at the output of the range discriminator of the ATR system. Thus, when the target velocity interval is τ = 0.2 s, the target velocity is νp = 500 km/h, and the probing pulse duration varies from 1 to 0.1 μs, the correlation coefficient of the inter-period fluctuations of the passive interference r м (nT ) decreases from 0.83 to 0.67, resulting in a decrease in the suppression coefficient of the passive interference by the MTS system. Conclusion. The results obtained can be used when designing pulse Doppler radars using interference-proof systems for measuring the parameters of moving targets.

Background. Technologies for the production of unmanned aerial vehicles (UAVs) of various classes are rapidly developing in Ukraine and the world. Small in terms of weight and dimensions and almost invisible for most information and measurement systems, UAVs began to be used in various industries - from the national economy to multimedia and advertising. Together with their useful application, new dangers and incidents have appeared - a collision of UAVs with people, structures, cultural monuments, the transportation of criminal goods, terrorist acts, flights over prohibited areas and within airports. UAV detection and control of their movement in populated areas and near critical objects are becoming one of the most important tasks of air traffic control services. The existing systems of the optical, acoustic and radar ranges cannot effectively perform such tasks in difficult meteorological conditions. As an addition to the already developed detection systems, it is proposed to use radiometric systems that register the UAV's own radio-thermal radiation. The authors have developed the theoretical foundations for the construction of multifrequency complexes necessary for the specification of their structural schemes. Objective. The purpose of the paper is development of a scheme for a multi-frequency radiometric complex for detecting UAVs in different meteorological conditions based on optimal algorithms. Methods. Analysis of the experience in the development of radiometric systems and methods for dealing with fluctuations in the gain of receivers, optimal operations for processing signals of intrinsic radio-thermal radiation, investigations of the probabilistic characteristics of detection and analysis of the domestic market of microwave technology developers will make it possible to develop a scheme of a multifrequency radiometric complex that will perform reliable measurements in various meteorological conditions. Results. A block diagram of a four-frequency radiometric complex is proposed, which can be implemented in practice and is capable of performing reliable measurements in various meteorological conditions. The frequencies 10 GHz, 20 GHz, 35 GHz, and 94 GHz were chosen as the resonant frequencies for tuning the radiometric receivers. For a given design and characteristics of receivers, the probabilities of detecting a UAV were calculated depending on the range of its flight. Conclusions. From the results of the analysis of the existing achievements in the development of radiometric systems in Ukraine and the elemental base of microwave components available on the market, it follows that the Ku and K bands have the worst characteristics of spatial resolution, but are all-weather. The Ka and W bands are highly sensitive to radio-thermal radiation against the background of a clear sky, but are completely "blind" in a cloudy atmosphere and in rain. The results of calculating the detection ranges with a probability of 0.9 lie in the range from 1 to 3 km, depending on the condition of the atmosphere. These results coincide with the known detection ranges of optical, acoustic and radar systems, but the selected parameters of the receivers do not correspond to potential world achievements and can be improved.

U NMANNED Aerial Vehicles (UAVs) hold good promise for autonomously carrying out complex civilian and military operations. However, many of these missions require them to fly at low altitudes, making them vulnerable to collision with both stationary as well as moving obstacles. Hence, it is vital that UAVs are equipped with autonomous capability to sense and avoid collisions, especially for the pop-up threats. When such a threat is sensed and a collision is predictedwithin a short time ahead, theUAV should be able to react and maneuver away quickly so that the collision is avoided. An algorithmwhich can assure such amaneuver is called a “reactive collision avoidance algorithm.” Since the available reaction time in such a scenario is usually is small and UAVs are usually limited by computational resources, such an algorithm should also be computationally very efficient (it should preferably be noniterative). It is also required that whilemaneuvering away, it should notmaneuver toomuch away from the obstacle either. This is both to avoid collision from other nearby obstacles and not to compromise on the overall mission objective. There are various attempts in the literature to develop algorithms for collision avoidance purpose, many of which are inspired from global path planning algorithms. The artificial potential field method is such an approach where the motion of the vehicle is guided under the influence of a potential field. The potential field (which is essentially a cost function) is designed in such a way that obstacles have repulsive fields while the destination has an attractive field. The safe path of the UAV is then found by optimizing the carefully selected cost function. To tune this basic philosophy for reactive collision avoidance, a model predictive control-based algorithm has been proposed in the literature. This algorithm essentially assures path following under safe conditions (i.e. if no collision is predicted in the near future) and invokes the potential field function when new collisions are sensed. However, in potential field based techniques the associated optimization process is typically done in an iterative manner. Because of this they are usually computationally intensive and hence are not suitable for reactive collision avoidance of airborne UAVs in general. A promising algorithm in collision avoidance and global path planning is the philosophy of rapidly-exploring random tree (RRT) [1], which has also been used for reactive collision avoidance. However, there are many concerns about the RRT approach, which can largely be attributed to the random nature of the algorithm. For example, the path predicted by RRT is usually a sting of connected straight lines that does not reflect the path followed by a vehicle with nonholonomic constraints. More important, it is a probabilistic approach and hence there is no guarantee of finding a feasible path within a limited finite time. Other graph search algorithms such as best-first search are also implemented for reactive collision avoidance [2]. However, this is not systematic approach and could result in the algorithm searching far too many nodes under some conditions. Moreover, precomputing motion primitives and saving them in a lookup table is infeasible for UAVs, which are usually resource-limited. An interesting perspective to collision avoidance problem is the minimum effort guidance [3], where an optimal control-based approach has been proposed after applying the collision cone philosophy to detect collisions. This method is computationally nonintensive as a closed form solution has been proposed. Even though this is an interesting idea, by minimizing the lateral acceleration, perhaps it imposes unwanted extra constraint on the problem formulation as reactive collision avoidance problems do not necessarily have to be carried out with minimum lateral acceleration. More important, one can observe that this formulation only assures position guarantee and no constraint is imposed on the velocity vector. Hence, even though it guides the vehicle to a carefully selected target point on the safety boundary (we call it the “aiming point”), it causes the vehicle to maneuver until this point. This can be risky as the vehicle may enter the safety ball before reaching the aiming point. Even though the collision cone based aiming point philosophy is a very good idea, the authors of this Note strongly believe that instead of only position guarantee, rather the velocity vector should be aligned towards the aiming point as soon as a collision is detected (which will automatically lead to position guarantee as well). Towards this objective, two new nonlinear guidance laws are proposed in this Note, which are named as nonlinear geometric guidance (NGG) and differential geometric guidance (DGG). These guidance laws are inspired by the philosophy of “aiming point guidance” (APG) [4], which has been proposed in missile guidance literature. It turns out that the APG is a simplified case of the NGG where the associated since function is replaced by its linear approximation (hence, for a systematic discussion, it is renamed as linear geometric guidance (LGG) in this Note). Both of the guidance algorithms proposed in this Note quickly align the velocity vector of the UAValong the aiming point within a part of the available time-togo, which ensures quick reaction and hence safety of the vehicle. The main feature of this philosophy is that they effect high maneuvering at the beginning, causing the velocity vector of the UAV to align with the aiming point direction quickly and then settling along it. Therefore there is no need to maneuver all the way until the aiming point is reached and hence the chance of the UAV entering into the safety ball is minimized. Using the point of closest approach (PCA) [5], the proposed NGG andDGGalgorithms have also been extended for collision avoidance with moving obstacles in both cooperative as well as ignorant scenarios. Mathematical correlations between the guidance laws have also been established, which show that the NGG and DGG are exactly correlated to each other with appropriate gain selections, while the LGG is an approximation of DGG. A “sphere-tracking algorithm” is also proposed in this Note where the UAV is guided to track the surface of the safety spherewhenever a brief violation of the safety boundary occurs after reaching the aiming point because of the location of the next aiming point (which may include the target in Presented as Paper 2010-8315 at the AIAA Guidance, Navigation and Control, Toronto, 2–5August 2010; received 26May 2010; revision received 4 October 2010; accepted for publication 6 October 2010. Copyright © 2010 by Radhakant Padhi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/11 and $10.00 in correspondence with the CCC. ∗Former Project Assistant, Department of Aerospace Engineering; anushamujumdar87@gmail.com. Associate Professor, Department of Aerospace Engineering; padhi@ aero.iisc.ernet.in. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 34, No. 1, January–February 2011

The need to develop a method for calculating the secondary radiation characteristics of thin conductors located under the dielectric shell of an unmanned aerial vehicle is due to the growing requirements for the efficiency of detection and identification of hidden objects in modern conditions. Unmanned aerial vehicles used in military and civilian applications are often equipped with thin conductors that serve as antennas, sensors, or structural elements. Their interaction with electromagnetic radiation is complicated by the presence of a dielectric shell, which affects the nature of the secondary radiation, changing its spectral and polarisation characteristics. Accurate analysis of such characteristics is critical for enabling effective remote sensing, development of radar and electronic warfare systems, and optimisation of unmanned aerial vehicles design to meet the requirements for their stealth and functionality. The development of modelling methods that take into account the specific geometry and material properties of conductors and sheaths allows us to obtain more accurate data required to solve applied problems in the scientific, technical and defence sectors. When assessing the secondary radiation of aircraft-type unmanned aerial vehicles, the main source of radiation is usually the internal equipment under a dielectric single or multilayer shell. However, insufficient attention is paid to the thin conductors inside the wing. These conductors can make a significant contribution to the overall scattered field. Due to the fact that the wing of an unmanned aerial vehicle has dimensions that are significantly larger than the wavelength of the irradiating radar stations, it is essential to use high-frequency asymptotic methods to calculate its secondary radiation. The paper proposes to solve the calculation of scattering on electrically thin conductors using the Pocklington equation. The calculations were based on integral representations obtained using Lorentz's lemma and the Pocklington equation for thin-wire antennas. The article develops a method for calculating the scattering characteristics of thin conductors under a dielectric shell (e.g., a wing console of an unmanned aerial vehicle), which takes into account the interaction between the dielectric shell and thin conductors in the said structure. The application of the presented calculation method can contribute to obtaining a priori information about the scattering characteristics of the elements of an unmanned aerial vehicle, which is important for the tasks of detecting and identifying objects, in particular in radar systems.

The use of unmanned aerial vehicles (UAVs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission, image processing and analysis, and accuracy assessment.

Unmanned aerial vehicle (UAV) technology is a rapidly growing field with tremendous opportunities for research and applications. To achieve true autonomy for UAVs in the absence of remote control, external navigation aids like global navigation satellite systems and radar systems, a minimum energy trajectory planning that considers obstacle avoidance and stability control will be the key. Although this can be formulated as a constrained optimization problem, due to the complicated non-linear relationships between UAV trajectory and thrust control, it is almost impossible to be solved analytically. While deep reinforcement learning is known for its ability to provide model free optimization for complex system through learning, its state space, actions and reward functions must be designed carefully. This paper presents our vision of different layers of autonomy in a UAV system, and our effort in generating and tracking the trajectory both using deep reinforcement learning (DRL). The experimental results show that compared to conventional approaches, the learned trajectory will need 20% less control thrust and 18% less time to reach the target. Furthermore, using the control policy learning by DRL, the UAV will achieve 58.14% less position error and 21.77% less system power.

In the unmanned aerial vehicle (UAV) based localization of slow-moving radio frequency (RF) sources with unknown transmitted strength of signal, such as a person with a cell phone in a search and rescue mission, the UAV navigation errors are significant sources of localization error. Although the use of a global positioning system (GPS) can reduce the UAV’s localization error significantly resulting in more accurate RF source localization, if the GPS signal is lost temporarily or permanently, the accuracy of the UAV-based localization decreases rapidly. In this paper, a simultaneous localization and mapping (SLAM)-inspired approach for simultaneous localization of UAV and RF sources (SLUS) is proposed. The proposed approach solves these two connected problems, i.e. the UAV localization and RF source localization, simultaneously to decrease the error of the UAV position estimation and the error of the RF source localization. In the proposed approach, beside the UAV position prediction, the RF source position prediction is also performed. Then the predicted states are augmented and the augmented predicted state information is corrected using range-ratio and bearing observations, i.e. RF source features, considering the unknown transmitted power. The proposed approach is simulated and the results show that the normal divergence of a target localization and the divergence of the UAV navigation in latitude and longitude channels have been eliminated using this approach. In other words, simultaneous localization of the UAV and RF sources uses the RF sources, as features in the environment, to aid the navigation system. Although the approach is similar to the mapping of RF sources in the environment, the created map would be useless after finding the RF sources. That is why SLUS has been used instead of SLAM. The main contributions of this work are: 1) performing simultaneous localization of a UAV and targets using RF signals, especially in a non-line of sight (NLOS) condition; 2) using difference of signal strength, i.e. differential received strength signal indicator (DRSSI), to eliminate the impact of unknown target signal power; and 3) simultaneous multi-target localization and tracking.

In recent years, unmanned aerial vehicles (UAVs) have become a dominant force in the aerospace industry. Recent technological developments have moved these aircraft from remote operation roles to more active response missions. Of particular interest is the possibility of applying UAVs toward solving complex problems in long-endurance missions. Under that belief, the feasibility of utilizing UAVs for wildfire detection and response was investigated in a partnership that included NASA’s Aeronautics Research Mission Directorate and Science Mission Directorate, and the United States Forest Service. Under NASA’s Intelligent Mission Management (IMM) project, research was conducted to develop a mission architecture that would enable use of a high altitude UAV to search for reported wildfires with a separate low altitude UAV supporting ground assets. This research proposes a “straw man” concept incorporating both a High Altitude Long Endurance (HALE) UAV and a Low Altitude Short Endurance (LASE) UAV in a loosely coupled, low cost solution tailored towards wildfire response. This report identifies the communications architecture, algorithms, and required system configuration that meets the outlined goals of the IMM project by mitigating wildfires and addressing the United States Forest Service immediate needs. The end product is a defined parent-child framework capable of meeting all wildfire mission goals. The concept has been implemented in simulation, the results of which are presented in this report.

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

The use of unmanned aerial vehicles (UAVs) in agriculture is an innovation for Russia and has great potential, especially when implementing the tasks of precision farming. The directions of using UAV in agriculture are considered. It is shown that for the differentiated application of fertilizers and pesticides, it is most expedient to use UAVs of helicopter and rotary-wing type flying at low speeds (30-40 km/h) and low altitudes (0.5-1.5 m) with a large payload (300-400 kg), as they most fully meet ecological and environmental requirements and ensure the safety of modern human-machine systems. The basic requirements to the quality of the technological operation for the variable rate application of fertilizers and pesticides with the help of unmanned aerial vehicles have been developed: the rate of application of fertilizer working fluids 50-200 l/ha with a discreteness of doses of 10-15 kg active fraction per hektar (25-40 l/ha), pesticides -10-20 l/ha with discreteness of 5 l/ha, median mass diameter of droplets - 250-300 mkm, uneven distribution of working fluid over the operating width is no more than 15 percent, demolition of working fluids outside the treated area is no more than 20 percent, time of control index setting at the boundaries of the allocated plots is no more than 1.1 s; operating speed of the flight is not more than 40 km/h, the altitude of the flight when applying fertilizers and pesticides equals 1.0-1.5 m.

Актуальность исследования обусловлена тем, чтов настоящее время в Российской Федерации для целейобслуживания добычи и разработки полезных ископаемых необходимо выполнять съемочные и геодезические работы,по результатам которых формируются массивыгеопространственных данных, которые являются основой для проектирования и обустройства нефтяных и газовых месторождений и являютсяисходной информацией для трехмерного моделирования земной поверхности, геологических структур месторождений и инфраструктуры трубопроводных систем. Кроме того,геопространственные данные необходимы для подготовки документов при постановке на государственный кадастровый учет и оформления прав землепользования. На основании этого в статье поставлена научно-техническая задача: проанализировать возможности применения различных способов обеспечения геопространственной информацией ГИС трубопроводных систем данными для 3D моделирования исследуемыхместорождений и трубопроводов.Корпоративные ГИСнаполняют данными о состоянии трубопроводного транспорта,используя информацию с космических съемочных систем с высоким и средним пространственным разрешением, а также материалы съемки с беспилотных летательных аппаратов и воздушного лазерного сканирования. Мониторинг трубопроводных систем осуществляют также с использованием данных наземных геодезических съемок.Одним из распространенных видов современного геодезического оборудования являются высокоточные и точные электронные тахеометры, позволяющие получать геопространственную информацию о местности и антропогенных объектах как в плане, так и по высоте. Высотная составляющая определяется в результате выполнения тригонометрического нивелирования. Применение способа тригонометрического нивелирования особенно актуально при работе в пересеченной местности и неблагоприятных условиях, к которым в частности относятся районы Крайнего Севера. Цель: проанализировать возможности применения различных способов обеспечения геопространственной информацией ГИС трубопроводных систем: космических съемок с различным пространственным разрешением, данных беспилотных летательных аппаратов и воздушного лазерного сканирования, тригонометрического нивелирования IIIиIV классов с применением высокоточных электронных тахеометровпри формировании массивов геопространственных данныхсистем мониторинга магистральных трубопроводов. Методы: дешифрирование аэрокосмических изображений, экспериментальные исследования в полевых условиях с материалами космических съемок, данными беспилотных летательных аппаратов и воздушного лазерного сканирования, использование высокоточных нивелиров и электронных тахеометров для тригонометрического нивелирования, геоинформационные технологии. Результаты.Выполнена оценка качества геопространственной информации для ГИС-мониторинга трубопроводных систем. Работа показала, что данные дистанционного зондирования Земли с различными пространственными разрешениями, беспилотных летательных аппаратов и воздушного лазерного сканированияпозволяют сформировать массивы геопространственных данных, необходимых для служб, эксплуатирующих трубопроводные системы, обеспечивающих плановую привязку с достаточной точностью; при создании высотного геодезического обоснования, когда точность необходимо повысить до III и IV классов, вместо трудоемкого геометрического нивелирования целесообразно дополнять массивы информацией, полученной методом тригонометрического нивелирования с использованием высокоточных электронных тахеометров.