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

Zhou, Yu; Shang, Song; Xing, Song; Zhang, Shiyu; You, Tianqi; Zhang, Linrang

doi:10.3390/rs14246220

Cited by 7 publications

(6 citation statements)

References 33 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It combines deep learning with extreme theory to estimate the probability of unknown classes. In [17], the authors used STFT and OpenMax to recognize different jamming signals. Although the DNN model and the source signal are different, the OpenMax-based recognition method in [17] is still instructive.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…In [17], the authors used STFT and OpenMax to recognize different jamming signals. Although the DNN model and the source signal are different, the OpenMax-based recognition method in [17] is still instructive. Therefore, we chose to reuse its OpenMax method accompanied by the recognition model in Section 2.3 as the baseline method.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…At the same time, our method could be applied to other radar signal recognition tasks. For example, in radar jamming recognition tasks [17,31], the library cannot include all of the jamming types from the target jamming emitter. Therefore, it is important to accurately classify the known jamming types and recognize the unknown jamming.…”

Section: Application Scenariosmentioning

confidence: 99%

See 2 more Smart Citations

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Yuan,

Li,

2023

Remote Sensing

View full text Add to dashboard Cite

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition.

show abstract

Section: Baseline Methodsmentioning

confidence: 99%

Section: Baseline Methodsmentioning

confidence: 99%

Section: Application Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Yuan,

Li,

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Thanks to the development of artificial intelligence technology, deep learning has been successfully applied to ISAR jamming pattern recognition [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. For instance, Wang et al [ 27 ] implemented the recognition of jamming patterns by CNN for three kinds of jamming, including suppression jamming, multiple false targets jamming, and narrow-pulse jamming.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network

Shi

Zhang

Lin

et al. 2023

Sensors

View full text Add to dashboard Cite

Micro-motion jamming is a new jamming method to inverse synthetic aperture radar (ISAR) in recent years. Compared with traditional jamming methods, it is more flexible and controllable, and is a great threat to ISAR. The prerequisite of taking relevant anti-jamming measures is to recognize the patterns of micro-motion jamming. In this paper, a method of micro-motion jamming pattern recognition based on complex-valued convolutional neural network (CV-CNN) is proposed. The micro-motion jamming echo signals are serialized and input to the network, and the result of recognition is output. Compared with real-valued convolutional neural network (RV-CNN), it can be found that the proposed method has a higher recognition accuracy rate. Additionally, the recognition accuracy rate is analyzed with different signal-to-noise ratio (SNR) and number of training samples. Simulation results prove the effectiveness of the proposed recognition method.

show abstract

“…In the actual open set jamming scenario, when a jamming pattern that does not exist in the training jamming library appears in the test environment, the existing radar jamming identification methods will incorrectly identify this unknown jamming as one of the known jamming patterns. In [26] Currently, OWR techniques have been applied in target recognition of synthetic aperture radar (SAR) images. In [27], a hierarchical embedding and incremental evolutionary network (HEIEN) was designed for when there are fewer unknown target training sets in open scenarios, which requires only a small number of unknown target samples for effective model training.…”

Section: Introductionmentioning

confidence: 99%

Radar Active Jamming Recognition under Open World Setting

Zhang,

Zhao,

2023

Remote Sensing

View full text Add to dashboard Cite

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.

show abstract

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

Cited by 7 publications

References 33 publications

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Radar Emitter Signal Intra-Pulse Modulation Open Set Recognition Based on Deep Neural Network

Recognition of Micro-Motion Jamming Based on Complex-Valued Convolutional Neural Network

Radar Active Jamming Recognition under Open World Setting

Contact Info

Product

Resources

About