Radar Active Jamming Recognition based on Recurrence Plot And Convolutional Neural Network

Lin, Junjie; Fan, Xiaolei

doi:10.1109/imcec51613.2021.9481990

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…The results show that the CNN has high operational efficiency for different damage location extracted from RPs to identify various vibration modes of the structure. More studies 24–28 applied RP to generate effective images and input the deep learning CNN models to classify targets, and they have confirmed that using RP with deep learning, especially CNN-based model, can effectively extract more features in various domain problems and improve the accuracy of prediction. To the best of our knowledge, there is rarely research to analyze the rotating wheel defects of railway based on the method of RP and CNN- model.…”

Section: Introductionmentioning

confidence: 91%

Condition monitoring for fault diagnosis of railway wheels using recurrence plots and convolutional neural networks (RP-CNN) models

Chung,

Lin

2023

Measurement and Control

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RPThe wheel condition monitoring when the train in operation is significant task to prevent the occurrence of unexpected event. In this study, the piezoelectric sensors were installed on the railway track to collect the dynamic voltage-and-strain signals when the train wheels pressed them. These one-dimensional time series signals were transformed to the two-dimensional Recurrence Plots (RP) images as an input data sets for two deep learning models, Xception and EfficientNet-B7. The binary classification, Normal or Faulty as the diagnostical output to indicate the health state of the train wheels in that time. Five metrics were selected to evaluate the performance of two models, namely Accuracy, Precision, Recall, Miss Rate, and AUC. The results show that both models perform the high accuracy of 91.1% to the wheel condition classification. Furthermore, EfficientNet-B7 shows better performance in Recall, Miss-rate, and AUC metrics than those of Xception to express the premium ability in defective wheel identification, which is crucial for this application. Therefore, the efficientNet-B7 is selected as a favorable machine learning classifier for the fault diagnosis of rolling stock wheels. It is significant contribution to train wheel condition monitoring and health management since it provides the effective diagnostic information for maintenance decision to decrease the occurrence of unexpected event.

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Section: Introductionmentioning

confidence: 91%

Condition monitoring for fault diagnosis of railway wheels using recurrence plots and convolutional neural networks (RP-CNN) models

Chung,

Lin

2023

Measurement and Control

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“…Lin et al. utilised the recursive image extraction method and achieved high recognition accuracy for eight types of jamming signals using a two‐dimensional CNN [13]. Xiao et al.…”

Section: Introductionmentioning

confidence: 99%

“…Shao et al developed a jamming classification model that is based on one-dimensional CNN and improved it using Siamese CNN and fusion networks [11,12]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Zou

Xie

Lin

2023

IET Radar Sonar & Navi

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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.

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“…A deep learningbased short-term voltage stability (STVSA) model has been proposed which achieves higher accuracy in the case of small samples compared with the previous methods [10]. The two categories of radar jamming recognition methods are feature extraction-based [11] and deep learning-based methods [12]. A convolutional neural network (CNN)-based radar jamming recognition method was proposed in 2019, first using the time-frequency images of the various jamming signals as the CNN inputs [13].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

et al. 2022

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Jamming recognition is an essential step in radar detection and anti-jamming in the complex electromagnetic environment. When radars detect an unknown type of jamming that does not occur in the training set, the existing radar jamming recognition algorithms fail to correctly recognize it. However, these algorithms can only recognize this type of jamming as one that already exists in our jamming library. To address this issue, we present two models for radar jamming open set recognition (OSR) that can accurately classify known jamming and distinguish unknown jamming in the case of small samples. The OSR model based on the confidence score can distinguish known jamming from unknown jamming by assessing the reliability of the sample output probability distribution and setting thresholds. Meanwhile, the OSR model based on OpenMax can output the probability of jamming belonging to not only all known classes but also unknown classes. Experimental results show that the two OSR models exhibit high recognition accuracy for known and unknown jamming and play a vital role in sensing complex jamming environments.

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Radar Active Jamming Recognition based on Recurrence Plot And Convolutional Neural Network

Cited by 9 publications

References 8 publications

Condition monitoring for fault diagnosis of railway wheels using recurrence plots and convolutional neural networks (RP-CNN) models

Condition monitoring for fault diagnosis of railway wheels using recurrence plots and convolutional neural networks (RP-CNN) models

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

Intelligent Radar Jamming Recognition in Open Set Environment Based on Deep Learning Networks

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