Radar Signal Recognition by CWD Picture Features

Tavakoli, Erfan; Falahati, Abolfazl

doi:10.4236/ijcns.2012.54031

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…Wu et al [15] used the time-frequency energy distribution features of STFT and SVM classifier for identification. E T et al [16] proposed the combination of biometric features extracted from CWD spectra with a pattern recognition algorithm for recognition.…”

Section: Research Background and Scientific Significancementioning

confidence: 99%

Radar Spectrum Image Classification Based on Deep Learning

Sun

Zheng

et al. 2023

Electronics

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With the continuous development and progress of science and technology, the increasingly complex electromagnetic environment and the research and development of new radar systems have led to the emergence of various radar signals. Traditional methods of radar emitter identification cannot meet the needs of current practical applications. For the purpose of classification and recognition of radar emitter signals, this paper proposes an improved EfficientNetv2-s classification method based on deep learning for more precise classification and recognition of radar radiation source signals. Using 16 different types of radar signal parameters from the signal parameter setting table, the proposed method generates random data sets consisting of spectrum images with varying amplitude. The proposed method replaces two-dimensional convolution in EfficientNetV2 with one-dimensional convolution. Additionally, the channel attention mechanism of the EfficientNetv2-s is optimized and modified to obtain attention weights without dimensional reduction, resulting in superior accuracy. Compared with other deep-learning image-classification methods, the test results of this method have better classification accuracy on the test set: the top1 accuracy reaches 98.12%, which is 0.17~3.12% higher than other methods. Furthermore, the proposed method has lower complexity compared to most methods.

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Section: Research Background and Scientific Significancementioning

confidence: 99%

Radar Spectrum Image Classification Based on Deep Learning

Sun

Zheng

et al. 2023

Electronics

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Interference classification and identification of TDCS based on improved convolutional neural network

2020

J. Phys.: Conf. Ser.

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A classification and recognition algorithm based on short-time Fourier transform and convolutional neural network (STFT-CNN) is proposed to solve the common interference signal classification and recognition problem in transform domain communication systems. In this algorithm, the time-spectrum diagram of interference signals obtained by short-time Fourier transform is input into the vggnet-16 network model improved according to STFT characteristics for feature learning and training, and the classification and recognition of signals are completed. Simulation results show that the proposed algorithm for comprehensive recognition rate reached 97.7%, 6 kinds of jamming signal in low SNR circumstance still can reach more than 93% recognition rate, compared with the traditional algorithm, this method not only improves the classification recognition rate of single interference, but also improves the recognition of mixed interference ability, has the ability to resist low signal-to-noise ratio, makes the transform domain communication system can choose transform domain for anti-interference, provides theoretical basis and support for the application of convolutional neural network in anti-interference of communication system in transform domain.

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