Specific Emitter Identification Based on Two Residual Networks

Qu, Lingzhi; Yang, Junan; Liu, Hui; Huang, Keju; Hu, Pengjiang; Li, Xiang; Zhang, Yu

doi:10.1109/imcec51613.2021.9482046

Cited by 3 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [ 19 ] used the features extracted by the residual network and complex-valued residual network as the real and imaginary parts of the classifier, respectively. The method works well when there are fewer labeled samples.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, data-driven strategy based on massive data and deep learning has become popular. A lot of studies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] have confirmed that deep learning has a strong feature extraction ability, which makes it superior to traditional methods in many recognition tasks. However, these works are mainly based on improved or new proposed network models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

Liu

Guo

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight iridium targets, respectively. Compared to similar research works, our method has obvious advantages.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

Liu

Guo

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Transforming signals into a form recognizable by neural networks is crucial for applying deep learning models to feature extraction algorithms [8]. Reference [9] proposed a feature fusion method that fully utilizes a small amount of labeled data by combining residual network learning models and complexvalued residual network learning models. This method effectively identi es communication radio stations while maintaining model convergence, further improving the capability of individual identi cation of communication radiation sources in scenarios with few labeled samples.…”

Section: Introductionmentioning

confidence: 99%

Identification of subtle features of communication radiation sources based on point density "painting"

Sun,

Li,

Zhang

2024

Preprint

View full text Add to dashboard Cite

In the face of the increasingly complex wireless communication environment, the traditional communication radiation source individual subtle feature recognition algorithm has the defects of poor real-time performance and low accuracy when dealing with the radiation source signal of small samples. Aiming at this problem, a subtle feature recognition method for communication radiation sources based on point density "painting" is proposed. Firstly, a signal feature extraction algorithm based on improved heat map is proposed to improve the accuracy and real-time performance of the traditional algorithm in extracting features when dealing with small sample signals. Second, based on the I/Q scatterplot, a fusion feature extraction algorithm based on the signal heatmap and scatterplot is proposed, which combines the region division strategy and the clustering fusion algorithm to extract the fusion features of the signal in an early fusion manner. Again, to address the problem of low recognition rate of fusion features, we introduce the concept of user portrait in the marketing field, and innovatively propose an algorithm for recognizing individual subtle features of communication radiation sources based on point density "portrait". Finally, a convolutional neural network (CNN) model is constructed to train the extracted point density image data of different classes of complex baseband signals, and the measured WIFI data collected in the darkroom environment of the laboratory is used to verify the effectiveness of the algorithm. The simulation results show that the algorithm can improve the accuracy by 1–4%, with a recognition rate of 100%, and exhibits good stability compared to the feature extraction algorithm based on the fusion feature map of signal heat map, I/Q scatter plot and signal heatmap .This provides an effective and reliable solution for the accurate identification of individual subtle features of communication radiation sources with small samples in complex environments.

show abstract

Unsupervised time series pattern recognition for purpose of electronic surveillance

Horky

Prokeš

Hubacek

2022

2022 24th International Microwave and Radar Conference (MIKON)

View full text Add to dashboard Cite

Specific Emitter Identification Based on Two Residual Networks

Cited by 3 publications

References 24 publications

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

A Novel Method for Recognizing Space Radiation Sources Based on Multi-Scale Residual Prototype Learning Network

Identification of subtle features of communication radiation sources based on point density "painting"

Unsupervised time series pattern recognition for purpose of electronic surveillance

Contact Info

Product

Resources

About