Performance comparison of neural network and statistical discriminant processing techniques for automatic modulation recognition

1991 ◽  
Author(s):  
Peter C. Hill ◽  
Gabriel R. Orzeszko
Keyword(s):  
Neural Network ◽  
Performance Comparison ◽  
Modulation Recognition ◽  
Automatic Modulation Recognition ◽  
Processing Techniques

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 Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification

2021 ◽  
Vol 2083 (4) ◽  
pp. 042092
Author(s):  
Zixi Li
Keyword(s):  
Neural Network ◽  
Signal To Noise Ratio ◽  
Feature Representation ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Modulation Recognition ◽  
Data Set ◽  
Communication Signals ◽  
Communication Signal ◽  
Automatic Modulation Recognition

Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.

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