scholarly journals Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4320
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Short Wave ◽  
Radio Station ◽  
Communication Behavior ◽  
Communication Behaviors ◽  
Training Time ◽  
Wave Radio ◽  
Bispectral Estimation ◽  
Model Algorithm

This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value.

scholarly journals Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96055-96064 ◽  
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei ◽  
Hao Xiong
Keyword(s):  
Short Wave ◽  
Radio Station ◽  
Wave Radio

scholarly journals Simple simulation training system for short-wave radio station

2018 ◽  
Author(s):  
Xianglin Tan ◽  
Zhichao Shao ◽  
Jianhua Tu ◽  
Fuqi Qu
Keyword(s):  
Simulation Training ◽  
Short Wave ◽  
Training System ◽  
Radio Station ◽  
Wave Radio ◽  
Simple Simulation

scholarly journals Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4270 ◽  
Author(s):  
Zilong Wu ◽  
Hong Chen ◽  
Yingke Lei
Keyword(s):  
Short Wave ◽  
Recognition Algorithm ◽  
Radio Station ◽  
Training Set ◽  
Generative Adversarial Network ◽  
Radio Stations ◽  
Real Samples ◽  
Adversarial Network ◽  
Wave Radio ◽  
Training Samples

It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

Atmospheric Effects on Short-Wave Radio Propagation

Nature ◽  
1949 ◽  
Vol 163 (4155) ◽  
pp. 958-959
Author(s):  
J. S. MCPETRIE ◽  
B. J. STARKEY
Keyword(s):  
Short Wave ◽  
Radio Propagation ◽  
Atmospheric Effects ◽  
Wave Radio

scholarly journals Analysis on the Influence of Transmission Lines span on Passive Interference in Shortwave

2018 ◽  
Vol 64 ◽  
pp. 05004
Author(s):  
Ying Lu ◽  
Zhibin Zhao ◽  
Jian gong Zhang ◽  
Zheyuan Gan
Keyword(s):  
Electromagnetic Field ◽  
Transmission Line ◽  
Transmission Lines ◽  
Electromagnetic Scattering ◽  
Electromagnetic Waves ◽  
Short Wave ◽  
Critical Distance ◽  
Observation Point ◽  
Radio Station ◽  
Passive Interference

The passive interference of transmission lines to nearby radio stations may affect the effective reception and transmission of radio station signals. Therefore, the accurate calculation of the electromagnetic scattering of transmission lines under the condition of external electromagnetic waves is the basis for determining the reasonable avoidance spacing of the two. For passive stations operating in short-wave frequencies, passive interference is mainly generated by the tower, and span is one of the most significant factors affecting passive interference. This paper uses the method of moments to carry out the passive interference calculations under normal circumstances, expounds the method of calculating the electromagnetic field of transmission line at the same time. And elaborates the method for calculating the electromagnetic field of the transmission line, obtains the space electric field intensity of the transmission line at the same working frequency and space location of the plane wave. Applying the approximate formula to calculate the formula for the span and critical distance between the observation point and the transmission line.

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