Active jamming recognition based on bilinear EfficientNet and attention mechanism

Xiao, Yao; Zhou, Jingyi; Yu, Yang; Guo, Limin

doi:10.1049/rsn2.12089

Cited by 11 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Common time‐frequency transformation methods include short‐time Fourier transform (STFT) and smooth pseudo Wigner‐Ville distribution (SPWVD). SPWVD is applied to the time‐frequency transformation of jamming signals due to its higher resolution [14] compared to STFT. However, as long as the resolution is suitable, STFT can also have good imaging effects although the resolution is not as high as SPWVD.…”

Section: Jamming Signal Simulation and Preprocessingmentioning

confidence: 99%

“…Xiao et al. proposed an efficient bilinear EfficientNet‐B3 network combined with an attention mechanism to identify eight types of active radar jamming signals [14]. Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

“…Lin et al utilised the recursive image extraction method and achieved high recognition accuracy for eight types of jamming signals using a twodimensional CNN [13]. Xiao et al proposed an efficient bilinear EfficientNet-B3 network combined with an attention mechanism to identify eight types of active radar jamming signals [14]. Zhang et al developed a complex CNN for radar jamming signal recognition [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Light‐weight deep learning method for active jamming recognition based on improved MobileViT

Zou

Xie

Lin

2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

In recent years, there has been significant research focused on identifying jamming signals using neural networks to improve identification accuracy. However, traditional networks require large‐scale datasets for training, which is challenging in small sample scenarios such as actual battlefield jamming signal data. Additionally, the large number of parameters in traditional models presents deployment challenges for devices. To address these issues, this study proposes a lightweight neural network model called MobileViT_CA, an improved version of MobileViT, for jamming recognition in small sample scenarios. MobileViT_CA adopts a coordinate attention mechanism suitable for lightweight networks, which further improves the classification performance of the network. The model converts one‐dimensional jamming signals into two‐dimensional time‐frequency distribution images through time‐frequency analysis and then uses the network for recognition. Compared with traditional neural network models, MobileViT_CA significantly reduces the network parameter volume while maintaining high recognition accuracy, accelerating network inference speed, and reducing hardware requirements. Experimental results show that MobileViT_CA performs well in small sample data. The model's fast learning ability has also been verified by using transfer learning, and the use of Grad‐CAM and t‐SNE to analyse MobileViT_CA demonstrates its advantages in feature recognition and extraction. Overall, this approach has the potential to significantly improve the reliability and effectiveness of jamming recognition systems in battlefield scenarios.

show abstract

Section: Jamming Signal Simulation and Preprocessingmentioning

confidence: 99%

“…Xiao et al. proposed an efficient bilinear EfficientNet‐B3 network combined with an attention mechanism to identify eight types of active radar jamming signals [14]. Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Light‐weight deep learning method for active jamming recognition based on improved MobileViT

Zou

Xie

Lin

2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…After the convolution operation, the maximum pooling is used to take out the maximum value of the feature vector in the corresponding convolution kernel as the most important feature [23].…”

Section: Modified Cbow Model and Network Structure Of Cnnmentioning

confidence: 99%

A Hybrid Approach for Network Rumor Detection Based on Attention Mechanism and Bidirectional GRU Model in Big Data Environment

Zhang

Guan

2022

Mobile Information Systems

View full text Add to dashboard Cite

In view of the low accuracy of Internet rumor detection in traditional machine learning detection methods due to the influence of comment directivity and incomplete features caused by too long sequences, an Internet rumor detection method combining attention mechanism and bidirectional gated recurrent unit (GRU) model in big data environment is proposed. First, the continuous bag of words (CBOW) model is modified by defining the context window and introducing the weighting module, and it is used to obtain the word vectors of the text. Then, the word vectors are used as the word embedding layer which is fed into a bidirectional GRU combined with an attention mechanism through the convolutional layer and pooling layer of the convolutional neural network (CNN) to fully extract the features of the text. Finally, the text feature vectors are input into the Softmax classifier for classification so as to realize the detection of Internet rumors. Based on the selected microblog dataset, the experimental results of the proposed model show that when the learning rate is set to 0.015, Accuracy, Precision, Recall, and F1 values are about 95.8%, 93.1%, 92.9% and 93.5%, respectively, which are better than other comparison models.

show abstract

“…time and calculation parameters. Xiao et al [21] proposed a model based on the lightweight network EfficientNet, adding bilinear pooling and attention mechanism to improve the recognition accuracy of jamming signals. Because the images of tomato leaves are fine-grained, and the appearance of different tomato disease species is very similar, the recognition rate of the classic convolutional neural network is not high.…”

Section: Introductionmentioning

confidence: 99%

Tomato leaf disease identification based on attention mechanism and bilinear pool

Xiao

Fang

Zheng

et al. 2022

Preprint

View full text Add to dashboard Cite

Aiming at the low recognition rate of current crop diseases by convolutional neural network, we propose the network In-SE-Bilinear model to recognize the tomato leaf diseases. In order to reduce the original number of network layers, GoogLeNet network is used as a back-bone. In the process of feature extraction, a Squeeze-and-Excitation (SE) block is connected after each Inception block, and a bilinear pooling layer is added before the connection layer, in front of which the convolution is used to reduce high-dimensional feature information. It is found that the accuracy rate is about 97% for identifying 16 disease classes, which is significantly better than the traditional convolutional neural network, and superior to Inception model, SE model, and bilinear model. Further, some evaluation indicators are calculated by the confusion matrix using test data set, and the results show that the specificity, recall rate and MIoU are 92.52%, 92.44% and 92.55%, respectively. The In-SE-Bilinear method can effectively improve the ability to recognize and classify tomato diseases.

show abstract

Active jamming recognition based on bilinear EfficientNet and attention mechanism

Cited by 11 publications

References 23 publications

Light‐weight deep learning method for active jamming recognition based on improved MobileViT

Light‐weight deep learning method for active jamming recognition based on improved MobileViT

A Hybrid Approach for Network Rumor Detection Based on Attention Mechanism and Bidirectional GRU Model in Big Data Environment

Tomato leaf disease identification based on attention mechanism and bilinear pool

Contact Info

Product

Resources

About