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

Wu, Bin; Yuan, Shibo; Li, Peng; Jing, Zehuan; Huang, Shao Ho; Zhao, Yaodong

doi:10.3390/s20216350

Cited by 43 publications

(25 citation statements)

References 24 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As the parameters, like carrier frequency, pulse width and bandwidth, are changing in a wider range, the testing dataset with a large number of samples could include as many situations as possible. Besides, the average classification accuracy of the well-trained model in [23] on a testing dataset containing 385,000 samples is almost 4% lower than that in a validation dataset containing 30,800 samples. Therefore, in order to increase the reliability of the classification and evaluate the real performance of the methods, we decide to use a large testing dataset.…”

Section: Dataset and Parameters Settingmentioning

confidence: 91%

Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

Yuan

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have been used in classification of intra-pulse modulation of radar emitter signals. However, those two-dimensional CNN-based methods, which require dimensional transformation of the original sampled signals in the stage of data preprocessing, are resource-consuming and poorly feasible. In order to solve these problems, we proposed a one-dimensional selective kernel convolutional neural network (1-D SKCNN) to accurately classify the intra-pulse modulation of radar emitter signals. Compared with other previous methods described in the literature, the data preprocessing of the proposed method merely includes zero-padding, fast Fourier transformation (FFT) and amplitude normalization, which is much faster and easier to achieve. The experimental results indicate that the proposed method has the advantages of faster speed in data preprocessing and higher accuracy in intra-pulse modulation classification of radar emitter signals.

show abstract

Section: Dataset and Parameters Settingmentioning

confidence: 91%

Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

Yuan

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Table 1, the simulated RMS parameters are the dynamic range [9]. To simulate the actual received signal, the modulated signal had the range of a certain parameter, and the noise was added.…”

Section: Experimental Datasetmentioning

confidence: 99%

“…Classification and recognition based on deep learning have many advantages. Without human assumptions and intervention about the features to be extracted, the deep neural network can effectively learn the features of the signals [9]. Deep learning can better resist the interference of noise in the extraction of signal features, thereby improving the generalization ability and accuracy in the identification of RMS [10].…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Modulation Recognition Based on Sep-ResNet

Mao

Ren

Yang

2021

Sensors

View full text Add to dashboard Cite

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.

show abstract

“…The optimized network improves the multilayer representation of features, and reduces the number of required samples. Wu et al [27] presented a novel attention-based one-dimensional (1D) CNN to extract more distinguishing features, and identify the signals from radar radiation sources. Specifically, the features of the given 1D signal series are extracted directly by the 1D convolutional layer, and weighed according to their importance to the recognition by the attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Liu¹,

Antypenko²,

Sushko³

et al. 2021

View full text Add to dashboard Cite

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

show abstract

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

Cited by 43 publications

References 24 publications

Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

Radar Signal Modulation Recognition Based on Sep-ResNet

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Contact Info

Product

Resources

About