scholarly journals Machine Learning for Modulation Classification of Radar Signals: A Survey

10.31645/17 ◽  
2019 ◽  
Author(s):  
Hitham Alshoubaki ◽  
Keyword(s):  
Neural Network ◽  
Modulation Classification ◽  
Computation Complexity ◽  
Modulation Recognition ◽  
Low Snr ◽  
The Neural Network ◽  
Military Applications ◽  
Automatic Modulation Recognition ◽  
Deep Learning Neural Network

Automatic modulation recognition of radar waveform is a major topic and has many military applications. This paper surveys the models and the techniques used in recognizing different modulation types of intercepted radar waveform. The literature shows the outstanding performance of deep learning neural network at low SNR values and in signal- overlapped environments as well. Additionally, using different mathematical and statistical algorithms demonstrated that utilized in features extraction of the data in order to feed them into the neural network improves the performance significantly. However, reducing computation complexity is in development too.

scholarly journals Implementation of Deep Learning-based Automatic Modulation Classifier on FPGA SDR Platform

Electronics ◽  
2018 ◽  
Vol 7 (7) ◽  
pp. 122 ◽  
Author(s):  
Zhi-Ling Tang ◽  
Si-Min Li ◽  
Li-Juan Yu
Keyword(s):  
Neural Network ◽  
Radio Spectrum ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
The Neural Network ◽  
Automatic Modulation Recognition ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
On Chip ◽  
Automatic Modulation Classifier

Intelligent radios collect information by sensing signals within the radio spectrum, and the automatic modulation recognition (AMR) of signals is one of their most challenging tasks. Although the result of a modulation classification based on a deep neural network is better, the training of the neural network requires complicated calculations and expensive hardware. Therefore, in this paper, we propose a master–slave AMR architecture using the reconfigurability of field-programmable gate arrays (FPGAs). First, we discuss the method of building AMR, by using a stack convolution autoencoder (CAE), and analyze the principles of training and classification. Then, on the basis of the radiofrequency network-on-chip architecture, the constraint conditions of AMR in FPGA are proposed from the aspects of computing optimization and memory access optimization. The experimental results not only demonstrated that AMR-based CAEs worked correctly, but also showed that AMR based on neural networks could be implemented on FPGAs, with the potential for dynamic spectrum allocation and cognitive radio systems.

scholarly journals Automatic Modulation Recognition Based on Hybrid Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qiang Duan ◽  
Jianhua Fan ◽  
Xianglin Wei ◽  
Chao Wang ◽  
Xiang Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Hybrid Neural Network ◽  
Memory And Attention ◽  
Computation Efficiency ◽  
Automatic Modulation Recognition ◽  
Short Time

Recognizing signals is critical for understanding the increasingly crowded wireless spectrum space in noncooperative communications. Traditional threshold or pattern recognition-based solutions are labor-intensive and error-prone. Therefore, practitioners start to apply deep learning to automatic modulation classification (AMC). However, the recognition accuracy and robustness of recently presented neural network-based proposals are still unsatisfactory, especially when the signal-to-noise ratio (SNR) is low. In this backdrop, this paper presents a hybrid neural network model, called MCBL, which combines convolutional neural network, bidirectional long-short time memory, and attention mechanism to exploit their respective capability to extract the spatial, temporal, and salient features embedded in the signal samples. After formulating the AMC problem, the three modules of our hybrid dynamic neural network are detailed. To evaluate the performance of our proposal, 10 state-of-the-art neural networks (including two latest models) are chosen as benchmarks for the comparison experiments conducted on an open radio frequency (RF) dataset. Results have shown that the recognition accuracy of MCBL can reach 93% which is the highest among the tested DNN models. At the same time, the computation efficiency and robustness of MCBL are better than existing proposals.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

scholarly journals Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Feng Wang ◽  
Shanshan Huang ◽  
Chao Liang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pulse Compression ◽  
Modulation Classification ◽  
Linear Kernel ◽  
Cognitive Radar ◽  
Feature Map ◽  
Time Frequency ◽  
Dimensional Plane

Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed. In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system. The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal. Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture. A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification. The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation. A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.

The Algorithm of Modulation Classification Based on Eigenface and Support Vector Machine in Low SNR

2014 ◽  
Vol 701-702 ◽  
pp. 442-448
Author(s):  
Xiang Ke Guo ◽  
Rong Ke Liu ◽  
Chang Yun Liu ◽  
Shao Hua Yue
Keyword(s):  
Support Vector Machine ◽  
Signal To Noise Ratio ◽  
Spectral Feature ◽  
Support Vector ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Low Snr ◽  
Feature Extract ◽  
Modulation Signal ◽  
Novel Algorithms

To improve the accuracy and reliability of modulation recognition at low signal to noise ratio (SNR) and few knowledge of signal parameter, the novel method based on the cyclic spectral feature and support vector machine(SVM) is presented. In the process of novel algorithms, the cyclic spectral analysis is used to realize the feature extract of the modulated signals, and the Eigenface method is used to reduce the amount of spectral coherence feature. Then, a new scheme of classification based on support vector machine is presented to classify the modulation signal. The experiment shows that the modulation classification accuracy of presented method is significantly improved at low SNR environment.

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