PC-based generation of real-time realistic synthetic scenes for low-altitude flights

2004 ◽  
Author(s):  
Erdal Yilmaz ◽  
H. Hakan Maras ◽  
Yasemin C. Yardimci
Keyword(s):  
Real Time ◽  
Low Altitude

Real-time UAV Trajectory Prediction for Safety Monitoring in Low-Altitude Airspace

2019 ◽  
Author(s):  
Matteo Corbetta ◽  
Portia Banerjee ◽  
Wendy Okolo ◽  
George Gorospe ◽  
Dmitry G. Luchinsky
Keyword(s):  
Real Time ◽  
Safety Monitoring ◽  
Trajectory Prediction ◽  
Low Altitude

scholarly journals Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 210
Author(s):  
Dongsuk Park ◽  
Seungeui Lee ◽  
SeongUk Park ◽  
Nojun Kwak
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Continuous Wave ◽  
Classification Model ◽  
Training Time ◽  
Fmcw Radar ◽  
Low Altitude ◽  
Real Time Identification ◽  
Short Time ◽  
Time Systems

With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification models learn features from data by themselves and have shown outstanding performance in computer vision tasks. In this paper, we propose a deep learning-based classification model that learns the micro-Doppler signatures (MDS) of targets represented on radar spectrogram images. To enable this, first, we recorded five LSS targets (three types of UAVs and two different types of human activities) with a frequency modulated continuous wave (FMCW) radar in various scenarios. Then, we converted signals into spectrograms in the form of images by Short time Fourier transform (STFT). After the data refinement and augmentation, we made our own radar spectrogram dataset. Secondly, we analyzed characteristics of the radar spectrogram dataset with the ResNet-18 model and designed the ResNet-SP model with less computation, higher accuracy and stability based on the ResNet-18 model. The results show that the proposed ResNet-SP has a training time of 242 s and an accuracy of 83.39%, which is superior to the ResNet-18 that takes 640 s for training with an accuracy of 79.88%.

SIMULATION OF REFLECTIONS FROM SURFACE-DISTRIBUTED OBJECTS ON EXAMPLE OF FRAGMENT OF UNDERLYING SURFACE

2019 ◽  
pp. 22-26
Author(s):  
M. A. Stepanov
Keyword(s):  
Real Time ◽  
Electromagnetic Waves ◽  
Geometric Model ◽  
Angular Position ◽  
Underlying Surface ◽  
Distributed Objects ◽  
Spherical Coordinate ◽  
Flight Mode ◽  
Straight Line ◽  
Low Altitude

The paper considers the problem of the synthesis of a low-point model of the relief of the underlying surface. The model can be used to conduct semi-null simulation when the radar is operating in low-altitude flight mode. A method for defining a relief in the form of a piecewise broken approximation is proposed. An algorithm is presented that allows real-time, for a given relief, to calculate for each of the elements of the resolution in range its angular position and angular dimensions. These parameters determine the expectation of the angular noise and the width of their probability density, respectively. The ability to work in real time is provided using a straight line in a spherical coordinate system when defining the relief. The recommendations on the choice of a geometric model from the previously justified family are given. The synthesized models provide a given form of the correlation functions of angular noise and adequately replace reflections from distributed objects. Geometric models can be used as the basis for matrix simulators of reflected electromagnetic waves.

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