Automatic recognition of radar signal types based on convolutional neural network

2019 ◽  
Author(s):  
Guoqing Ruan ◽  
Wei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Recognition ◽  
Radar Signal

scholarly journals Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6350
Author(s):  
Bin Wu ◽  
Shibo Yuan ◽  
Peng Li ◽  
Zehuan Jing ◽  
Shao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
One Dimensional ◽  
The Given ◽  
And Storage

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

scholarly journals Black Sigatoka Classification Using Convolutional Neural Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 323-326
Author(s):  
Cristian A. Escudero ◽  
◽  
Andrés F. Calvo ◽  
Arley Bejarano
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Tree ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Automatic Recognition ◽  
Black Sigatoka ◽  
Atypical Values ◽  
Leaf Image ◽  
Banana Crops

In this paper we present a methodology for the automatic recognition of black Sigatoka in commercial banana crops. This method uses a LeNet convolutional neural network to detect the progress of infection by the disease in different regions of a leaf image; using this information, we trained a decision tree in order to classify the level of infection severity. The methodology was validated with an annotated database, which was built in the process of this work and which can be compared with other state-of-the-art alternatives. The results show that the method is robust against atypical values and photometric variations.

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