A signal modulation classification algorithm based on convolutional neural network

2021 ◽  
Author(s):  
Yapin Wang ◽  
泽阳 刘 ◽  
xiaobin lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Algorithm ◽  
Modulation Classification ◽  
Signal Modulation

Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

2021 ◽  
Author(s):  
Chris Yakopcic ◽  
Tarek M. Taha ◽  
Sanjeevi Sirisha Karri ◽  
Guru Subramanyam ◽  
Aaron D. Smith ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Modulation Classification ◽  
Signal Modulation ◽  
Rf Signal

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.

scholarly journals Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibin Chang ◽  
Ying Cui
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classification Algorithm ◽  
Large Set ◽  
Data Set ◽  
Multilabel Learning ◽  
Average Maximum

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR-10 data set, which is much higher than the traditional CNN algorithm.

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