Optimization and analysis of air-to-ground wireless link parameters for UAV mounted adaptable Radar Antenna Array

The object of this study is the process of forming a minimally sufficient set of reference images for use in correlation-extreme navigation systems when changing the navigation parameters of unmanned aerial vehicles. The paper reports the results related to solving the task to form a set of reference images taking into account changes in the navigation parameters of high-speed unmanned aerial vehicles and their impact on the information features of images. The effect of changing the viewing angles and altitude on the formation of segmented images by energy characteristics has been studied. The discrete steps for navigation parameters were established, at which the correlation between image fragments is maintained at the level of 0.9. These values are from 90 to 120 meters in height and from 15° to 25° in angular parameters. The effect of the structure of segmented images on the selection of the reference object has been studied. It is shown that the feature of the selection of the reference object in the segmented image is the value of the fractal dimension 2.998…2.999. The study was conducted in the MATLAB software environment using the source image selected from Google Earth Pro. The application of the selected sequence of constructing fragments of reference images has made it possible to identify objects that have the best characteristics in terms of signal-to-noise ratio and structure with increasing discreteness of navigation parameters. The method differs from known ones in using the image structure as information features along with the brightness and contrast of objects. This would reduce the number of fragments of reference images while maintaining the accuracy indicator. The results could be implemented in secondary processing systems of correlation-extreme navigation systems used on high-speed unmanned aerial vehicles

In the last few years, valuable research in frequency diverse arrays (FDAs) has been conducted. However, emphasis had mainly been on uniform linear arrays (ULAs) and uniform rectangular arrays (URAs). Not much attention has been devoted to other configurations. Circular symmetry of uniform circular arrays (UCAs) has been an attraction to researchers for several reasons such as the ability to scan azimuthally through 360° and better spatial resolutions. Motivated by the potential advantages of UCA, this letter presents an in-depth investigation of circular geometries in FDA radars. In this letter theory, analysis and basic beam steering in uniform circular frequency diverse arrays (UCFDAs) is presented along with comparison with linear and rectangular counterparts.

In research, design and development of unmanned aerial vehicles (UAV), a key role belongs to automated measuring complexes for its simulation and prototyping electromechanical system with digital control. Design and debugging of such system are suggested and performed by using model-based approach with automated simulation tools. This approach means creating and using in further the simulation model of the measuring complex. In the paper, the simulation model for research parameters of unmanned aerial vehicles is discussed. This model is suggested to develop based on Gough–Stewart platform (six-axes platform manipulator) with UAV mounted on it. The researched model includes the UAV’s trajectory generator, automatic control device for UAV, kinematic manipulator model and decision subsystem to define the current attitude of the manipulator dynamic side. The model allows to evaluate the functioning parameters of proportional–integral–derivative controllers of spatial orientation angles, as well as to automatically obtain program code for implementing both automation of testing and the UAV control device. Also, the presented model can function in conjunction with the user interface of the measuring complex and can be used to evaluate the parameters of complex functioning.

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI UAV and growth parameters was shown. Correlation analysis between NDVI UAV and barley growth parameters revealed that NDVI UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI UAV , the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI UAV was applied to power function. From these results, NDVI UAV can be used as a new tool for monitoring barley growth.

Unmanned aerial vehicles (UAVs) are growingly used in different aspects of our lives. As the cost of UAVs is decreasing with novel technologies, their popularity is increasing rapidly in surveillance, disaster management, agriculture, military operations, etc. The recent trend of collaborative operations of a network of UAVs to achieve a common objective has attracted the researchers as well as commercial vendors. It has revolutionized the means of data collection to maximize mission performances. However, the collaborative UAVs, with respect to the mission objective, need to be resilient to the unavailability of one or more UAVs that can be caused due to cyberattacks or technical failures. As UAVs can easily be targeted by adversaries, these devices need to maintain safe communication with each other while avoiding fuel outage and mid-air collisions, thus reducing the possibilities of being hacked but remain undetected. In this work, we present a formal framework that takes different UAV parameters in a UAV network, resiliency requirements, and resource constraints as the input, and verifies for the resiliency threats. Each threat specifies if k or less number of UAVs fail, whether the rest of the network still can maintain successful communication. We illustrate the execution of this framework with an example case study. We evaluate the proposed framework to demonstrate the relationships between different parameters of a network and its resiliency. We also evaluate the framework in terms of the resiliency analysis performance and scalability.

This article investigates the achievable spectral efficiency (SE) of millimeter wave massive multiple-input multiple-output systems with three different array topologies deployed at the base station (BS), namely uniform rectangular array (URA), uniform linear array (ULA) or uniform circular array (UCA). To be practical, both small scale fading and large scale path loss, and shadowing effects are considered in the analysis. A 3D channel model taking both the azimuth and elevation domains into account is adopted for the analysis of the URA and UCA topologies. Considering randomly distributed users in the cell, we firstly deduce the distribution functions of the distances between the users and the BS, and the azimuth and elevation angles of departure, respectively. Based on the derived probability distribution, tight approximations on the achievable SE with maximum ration transmission under the three antenna array topologies are derived. Analytical results show that under all the three topologies, the total achievable SE increases with the number of BS antennas, but decreases with the radius of cell and decay exponent. Moreover, it converges to a saturated value in the high SNR regime and a large inter-element spacing. It is also found that the UCA configuration has a superior achievable SE than the URA and ULA cases, and thus is the best choice for mmWave massive MIMO systems in terms of the achievable SE.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

Investigating the path planning of multiple unmanned aerial vehicles (UAVs) covering multiple regions, this work proposes an effective heuristic method of region coverage path planning to reduce the complexity of the problem. The proposed method decomposes the solution process into two stages. First of all, the two most important parameters affecting the performance of UAV missions were considered, namely, the flying speed and the scan width. According to these two parameters of UAVs, a new multi-regional allocation scheme based on the minimum consumption ratio was proposed. With this allocation scheme, the coverage task allocation and path pre-planning of UAVs were obtained. Then, the UAVs’ trajectory routes were optimized based on the dynamic planning algorithm to reduce the time consumption of UAVs on the transfer path between regions. The method was evaluated with numerical experiments. The results showed that the proposed method can effectively solve the path planning problem of multiple UAVs covering multiple regions. Compared with an advanced algorithm, the time consumption for homogeneous and heterogenous UAV performance was reduced by 5.1% and 3%, respectively.

The unmanned aerial vehicle (UAV) is one of the mobile robots which has grown rapidly in popularity in recent years. UAVs have been used in various activities to convey important information on the aerial transportation system. However, an application to get real-time information from UAVs remains a challenging task. Therefore, this paper presents an integration system to optimise the use of UAVs for aerial surveillance. The proposed system explains the design and implementation a UAV system which consists of on-board and ground stations. The on-board system has the function to send UAV flight data, send aerial images and carry items using a gripper which equipped with sensors to detect obstacles. The ground station has the function to control UAV flights, receive and display UAV flight data and aerial images, display the attitude of the UAV and send commands to move the camera and gripper. Flight controllers in this system use non-linear complementary filter on SO(3) to estimate the attitude of the UAV and proportional-integral (PI) controller to stabilise the UAV flight. By providing the real-time acquisition data of the UAV, we believe this system can be a useful tool for many implementations using UAV.

With technological development of multi sensors, UAV (unmanned aerial vehicle) can identify and locate key targets in essential monitoring areas or geological disaster-prone areas by taking video sequence images, and precise positioning of the video sequence images is constantly a matter of great concern. In recent years, precise positioning of aerial images has been widely studied. But it is still a challenge to simultaneously realize precise, robust and dynamic positioning of UAV’s patrolling video sequence images in real time. In order to solve this problem, a visual positioning model for patrolling video sequence images based on DOM rectification is proposed, including a robust block-matching algorithm and a precise polynomial-rectifying algorithm. First, the robust block-matching algorithm is used to obtain the best matching area for UAV’s video sequence image on DOM (Digital Orthophoto Map), a pre-acquired digital orthophoto map covering the whole UAV’s patrolling region. Second, the precise polynomial-rectifying algorithm is used to calculate accurate rectification parameters of mapping UAV’s video sequence image to the best matching area obtained above, and then real time positioning of UAV’s patrolling video sequence images can be realized. Finally, the above two algorithms are analyzed and verified by three practical experiments, and results indicate that even if spatial resolution, surface specific features, illumination condition and topographic relief are significantly different between DOM and UAV’s patrolling video sequence images, proposed algorithms can still steadily realize positioning of each UAV’s patrolling video sequence image with about 2.5 m level accuracy in 1 s. To some extent, this study has improved precise positioning effects of UAV’s patrolling video sequence images in real time, and the proposed mathematical model can be directly incorporated into UAV’s patrolling system without any hardware overhead.

The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV's communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth

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.

This paper proposes the use of low-cost unmanned aerial vehicles (UAVs) to enable communication between mutually unreachable ground stations in regions without communication infrastructure. The proposed method involves flying UAVs at communicable altitude in a pre-specified pattern over the ground stations. A fully autonomous control system based on the proportional integral derivative controller and the non-linear dynamic inversion controller has been built for this purpose. A modified Ad-hoc On-Demand Distance Vector (AODV) protocol is used to route data packets between the ground stations over the network formed by the UAVs using a delay tolerant approach. Using simulations, we show the effectiveness of the proposed solution, which consistently achieves a packet delivery ratio of above 90% regardless of number of UAVs. The average end-to-end delay drops steeply with an increase in number of UAVs. For a separation distance of 2 km between ground stations, four low-cost UAVs with commodity WiFi communications equipment can achieve an average end-to-end delay of 15 s and packet delivery ratio of 95%, thus giving a good balance between number of UAVs and average end-to-end delay.

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.