scholarly journals METHODS AND MEANS OF COUNTERACTION OF ACOUSTIC AND OPTICAL RECONNAISSANCE UAV

2020 ◽  
Vol 6 (1) ◽  
pp. 149-154
Author(s):  
Nikita V. Ignatenko ◽  
Alexey N. Polikanin
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Systems ◽  
Multiple Objects ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Main Computer ◽  
Computer Attacks ◽  
Control Technologies ◽  
Combining Information

For the last few years, the ease of purchasing and using unmanned aerial vehicles (UAVs), their affordable cost has increased the demand for them both by companies and individuals. However, these devices might carry out illegal actions, starting with smuggling of illegal goods, unauthorized intelligence and computer attacks. As a result, this led to the urgency of developing effective and available countermeasures to detect and neutralize drones that perform reconnaissance of objects with confidential information. The most successful are autonomous systems for detecting and suppressing drones, which include optoelectronic, acoustic radar and radio frequency sensors, information from which is combined on the main computer to identify the threat and make further decisions. However, real-time monitoring is a rather difficult process that requires timely detection of adverse events or conditions. This creates many complex tasks such as object detection, classification, tracking multiple objects, and combining information from multiple sensors. In recent years, researchers have used various techniques to solve these problems and made notable progress. Applying deep learning to detect and classify UAVs is considered a new concept. In this regard, it became necessary to provide a generalized overview of UAV control technologies used for reconnaissance.

scholarly journals A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles

2021 ◽  
Vol 13 ◽  
pp. 175682932110168
Author(s):  
Hasan Karali ◽  
Gokhan Inalhan ◽  
M Umut Demirezen ◽  
M Adil Yukselen
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Computationally Intensive ◽  
Lifting Line ◽  
Lifting Line Method ◽  
Nonlinear Aerodynamic

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles. First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and post-stall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics. This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of unmanned aerial vehicles. The major novel feature of this model is that it can predict the aerodynamic properties of unmanned aerial vehicle configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of an unmanned aerial vehicle over a wide angle of attack range on the order of milliseconds, whereas computational fluid dynamics solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example.

Extended Kalman Filter Based Quadrotor State Estimation Based on Asynchronous Multisensor Data

2015 ◽  
Author(s):  
Mohammad Sarim ◽  
Alireza Nemati ◽  
Manish Kumar ◽  
Kelly Cohen
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Extended Kalman Filter ◽  
Real World ◽  
Flight Test ◽  
Communication Delays ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Multisensor Data

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

scholarly journals Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (3) ◽  
pp. 924 ◽  
Author(s):  
Duona Zhang ◽  
Wenrui Ding ◽  
Baochang Zhang ◽  
Chunyu Xie ◽  
Hongguang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Aerial Vehicles

scholarly journals Urban Natural Environment Analysis through Deep Learning Techniques on Automatically Acquired Images from Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Marco A Moreno-Armendáriz ◽  
Hiram Calvo ◽  
Carlos A Duchanoy ◽  
Anayantzin P López-Juárez ◽  
Israel A Vargas-Monroy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Natural Environment ◽  
Environment Analysis ◽  
Aerial Vehicles ◽  
Learning Techniques

scholarly journals Secure UAV-Based System to Detect Small Boats Using Neural Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Moisés Lodeiro-Santiago ◽  
Pino Caballero-Gil ◽  
Ricardo Aguasca-Colomo ◽  
Cándido Caballero-Gil
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
High Sensitivity ◽  
Search And Rescue ◽  
Detection Systems ◽  
Aerial Vehicles ◽  
Current Detection

This work presents a system to detect small boats (pateras) to help tackle the problem of this type of perilous immigration. The proposal makes extensive use of emerging technologies like Unmanned Aerial Vehicles (UAV) combined with a top-performing algorithm from the field of artificial intelligence known as Deep Learning through Convolutional Neural Networks. The use of this algorithm improves current detection systems based on image processing through the application of filters thanks to the fact that the network learns to distinguish the aforementioned objects through patterns without depending on where they are located. The main result of the proposal has been a classifier that works in real time, allowing the detection of pateras and people (who may need to be rescued), kilometres away from the coast. This could be very useful for Search and Rescue teams in order to plan a rescue before an emergency occurs. Given the high sensitivity of the managed information, the proposed system includes cryptographic protocols to protect the security of communications.

scholarly journals A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1136 ◽  
Author(s):  
Jun Yang ◽  
Arun Geo Thomas ◽  
Satish Singh ◽  
Simone Baldi ◽  
Ximan Wang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Formation Control ◽  
Physical Simulation ◽  
3D Visualization ◽  
Autonomous Systems ◽  
Level Control ◽  
Control Laws ◽  
Ongoing Research ◽  
Control Loops ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have multi-domain applications, fixed-wing UAVs being a widely used class. Despite the ongoing research on the topics of guidance and formation control of fixed-wing UAVs, little progress is known on implementation of semi-physical validation platforms (software-in-the-loop or hardware-in-the-loop) for such complex autonomous systems. A semi-physical simulation platform should capture not only the physical aspects of UAV dynamics, but also the cybernetics aspects such as the autopilot and the communication layers connecting the different components. Such a cyber-physical integration would allow validation of guidance and formation control algorithms in the presence of uncertainties, unmodelled dynamics, low-level control loops, communication protocols and unreliable communication: These aspects are often neglected in the design of guidance and formation control laws for fixed-wing UAVs. This paper describes the development of a semi-physical platform for multi-fixed wing UAVs where all the aforementioned points are carefully integrated. The environment adopts Raspberry Pi’s programmed in C++, which can be interfaced to standard autopilots (PX4) as a companion computer. Simulations are done in a distributed setting with a server program designed for the purpose of routing data between nodes, handling the user inputs and configurations of the UAVs. Gazebo-ROS is used as a 3D visualization tool.

scholarly journals UAV-g 2019: Unmanned Aerial Vehicles in Geomatics

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 74 ◽  
Author(s):  
Nex
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Navigation ◽  
Aerial Vehicles ◽  
Surrounding Environment ◽  
Poster Sessions ◽  
Aerial Vehicle ◽  
The University ◽  
The Impact

Unmanned aerial vehicle in geomatics (UAV-g) is a well-established scientific event dedicated to UAVs in geomatics and remote sensing. In the different editions of the journal, new scientific challenges have increased their synergy with adjacent domains, such as robotics and computer vision, thereby increasing the impact of this conference. The 2019 edition has been hosted by the University of Twente (The Netherlands) and has attracted about 300 participants for the full three-day program. Researchers from 36 different countries (from all continents) have presented 89 accepted papers in 17 oral and 2 poster sessions. The presented papers covered multi-disciplinary topics, such as photogrammetry, natural resources monitoring, autonomous navigation, and deep learning. All these contributions have in common the use of UAV platforms for the innovative acquisition and processing of the acquired data and information extracted from the surrounding environment.

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