scholarly journals Evaluation of MAV/UAV Collaborative Combat Capability Based on Network Structure

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jieru Fan ◽  
Dongguang Li ◽  
Rupeng Li
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Evaluation Method ◽  
Quantitative Description ◽  
Collaborative Network ◽  
Application Research ◽  
Military Research ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Process Node ◽  
Control Capability

The collaborative combat of manned/unmanned aerial vehicles (MAVs/UAVs) is a popular topic in combat application research. It maximizes the autonomous combat capability of UAVs and the control capability of MAVs. Furthermore, it improves the comprehensive combat effectiveness. The quantitative description of intercommunication in different aircrafts along with the evaluation of the collaborative combat capability is an emphasis in military research. This paper analyzes the collaborative combat process. Node and edge models are established in the MAV/UAV collaborative network. The intercommunication and combat behaviors among combat entities are analyzed. Based on the information entropy, the effect of capability uncertainties on the collaborative combat is described quantitatively. An evaluation method of the MAV/UAV collaborative combat capability is proposed. Finally, an example is given to demonstrate the proposed model and evaluation method that prove its feasibility and effectiveness.

scholarly journals The method of maximum plausibility in the problems of protection of the radio signal of unmanned aerial vehicles control

2020 ◽  
Vol 44 (4) ◽  
Author(s):  
S. I. Ganusyak ◽  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Radio Signal ◽  
Harmonic Oscillation ◽  
Radio Communication ◽  
Noise Interference ◽  
Communication Signal ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Method Of Maximum Likelihood ◽  
Aerial Vehicle

The paper considers the problem of protection of control of the radio signal of control of unmanned aerial vehicles as a task of control of signal parameters. The problem of control of a parameter of a radio signal and a problem of protection against interception by control of the unmanned aerial vehicle is formulated. The methods of estimating the parameters of the radio communication signal are analyzed and the method of estimating the parameters based on the method of maximum likelihood is proposed. This technique is demonstrated by estimating the phase of harmonic oscillation, which describes the radio signal and there are noise interference. Simulation modeling of the developed method is carried out, which confirmed the adequacy of the proposed model.

scholarly journals ViTT: Vision Transformer Tracker

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5608
Author(s):  
Xiaoning Zhu ◽  
Yannan Jia ◽  
Sun Jian ◽  
Lize Gu ◽  
Zhang Pu
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Language Processing ◽  
Global Context ◽  
Aerial Vehicles ◽  
Object Locations ◽  
Proposed Model ◽  
Shared Network ◽  
Complex Computer ◽  
Multi Uav

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.

scholarly journals The model of UAV formation based on the uniform allocation of points on the sphere

2018 ◽  
Vol 161 ◽  
pp. 03022 ◽  
Author(s):  
Mikhail Khachumov ◽  
Vyacheslav Khachumov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Uniform Distribution ◽  
Mathematical Apparatus ◽  
Optimal Assignment ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Distribution Of Points ◽  
Clustering Problems

We consider an approach to constructing the desired virtual structure, which should be formed by unmanned aerial vehicles (UAVs). The proposed model is based on the principle of quasi-uniform allocation of points, previously used by the author in clustering problems. The mathematical apparatus for solving the problem of forming the desired structure is given: necessary theorems are proved; the hypothesis on the uniform distribution of points on the boundary of the circle and the sphere is put forward and partially proved. The method for optimal assignment of UAVs to goal positions in the desired formation is considered.

scholarly journals Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 63
Author(s):  
Christoph Steup ◽  
Simon Parlow ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Systems ◽  
Energy Model ◽  
Generic Model ◽  
Extended Model ◽  
Generic Models ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Typical Behaviour ◽  
Set Up

The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%.

scholarly journals Improvement of the model of object recognition in aero photographs using deep convolutional neural networks

2021 ◽  
Vol 5 (2 (113)) ◽  
pp. 6-21
Author(s):  
Vadym Slyusar ◽  
Mykhailo Protsenko ◽  
Anton Chernukha ◽  
Pavlo Kovalov ◽  
Pavlo Borodych ◽  
...  
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Unmanned Aerial Vehicles ◽  
Convolutional Neural Networks ◽  
Aerial Photographs ◽  
Problem Solution ◽  
Deep Convolutional Neural Networks ◽  
Test Set ◽  
Aerial Vehicles ◽  
Proposed Model

Detection and recognition of objects in images is the main problem to be solved by computer vision systems. As part of solving this problem, the model of object recognition in aerial photographs taken from unmanned aerial vehicles has been improved. A study of object recognition in aerial photographs using deep convolutional neural networks has been carried out. Analysis of possible implementations showed that the AlexNet 2012 model (Canada) trained on the ImageNet image set (China) is most suitable for this problem solution. This model was used as a basic one. The object recognition error for this model with the use of the ImageNet test set of images amounted to 15 %. To solve the problem of improving the effectiveness of object recognition in aerial photographs for 10 classes of images, the final fully connected layer was modified by rejection from 1,000 to 10 neurons and additional two-stage training of the resulting model. Additional training was carried out with a set of images prepared from aerial photographs at stage 1 and with a set of VisDrone 2021 (China) images at stage 2. Optimal training parameters were selected: speed (step) (0.0001), number of epochs (100). As a result, a new model under the proposed name of AlexVisDrone was obtained. The effectiveness of the proposed model was checked with a test set of 100 images for each class (the total number of classes was 10). Accuracy and sensitivity were chosen as the main indicators of the model effectiveness. As a result, an increase in recognition accuracy from 7 % (for images from aerial photographs) to 9 % (for the VisDrone 2021 set) was obtained which has indicated that the choice of neural network architecture and training parameters was correct. The use of the proposed model makes it possible to automate the process of object recognition in aerial photographs. In the future, it is advisable to use this model at ground stations of unmanned aerial vehicle complex control when processing aerial photographs taken from unmanned aerial vehicles, in robotic systems, in video surveillance complexes and when designing unmanned vehicle systems

Development of thickened semi-synthetic engine oil M-5z / 20 AERO for four-stroke gasoline engines aircraft piston of unmanned aerial vehicles (UAVs)

2020 ◽  
Vol 06 ◽  
pp. 44-47
Author(s):  
A.A. Moykin ◽  
◽  
A.S. Medzhibovsky ◽  
S.A. Kriushin ◽  
M.V. Seleznev ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Engine Oil ◽  
Motor Oil ◽  
Gasoline Engines ◽  
State Research Institute ◽  
Technical Requirements ◽  
Aerial Vehicles ◽  
State Research ◽  
Industrial Batch ◽  
The Russian Federation

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.

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