skip to main content
10.1145/3632314.3632321acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisiaConference Proceedingsconference-collections
research-article

Radar Signal Recognition Method based on Transfer Learning

Published: 09 December 2023 Publication History

Abstract

Radar signal recognition has a guiding role for electronic warfare systems. The traditional radar signal recognition method confirms the target model through signal feature comparison, which is suitable for electromagnetic environments with single radar signal and low signal feature complexity. With the continuous development of radar technology, traditional radar signal recognition methods have been difficult to adapt to target recognition in complex electromagnetic environments. In order to improve the accuracy of radar signal recognition in complex electromagnetic environments, this paper proposed a radar signal recognition method based on transfer learning. At first, the proposed method performed pre-processing operations on the initial signal data collected by the sensor, including normalization, noise truncation, feature extraction, etc., and established a database for the processed signal data. Afterwards, a feedforward BP neural network model was trained to classify the signal data. Finally, on the basis of the transfer learning, pulse characteristic parameters (radio frequency, etc.) were added to the output layer of the feedforward BP neural network, it served as a new input layer to train deeper network models, making it adaptive and deviation-resistant. The feasibility of the proposed method was verified with six sets of different sample data. The results showed that the average recognition accuracy of the method in this paper reached 93.3%, compared with the traditional ANN-based method, the average recognition accuracy is improved by 12%, which verifies the reliability of the proposed method.

References

[1]
Atul Adya, Paramvir Bahl, Jitendra Padhye, Alec Wolman, and Lidong Zhou. 2004. A multi-radio unification protocol for IEEE 802.11 wireless Wang J F, Liu X Z. Development in SAR moving-target detection [J]. Journal of Shanghai Jiaotong University, 2018(10):7.
[2]
Quan D Y, Chen Y, Tang Z Y, Radar signal recognition based on dual channel convolutional neural network [J]. Journal of Shanghai Jiaotong University, 2022, 56(7):9.
[3]
Sun D H. Review of foreign electronic warfare development and thinking of Chinese electronic warfare research [J]. Shipboard Electronic Countermeasure, 2003(01):1-6.
[4]
Qin C H, Xue J. A method for specific emitter identification of radar based on the rising edge of pulse [J]. Shipboard Electronic Countermeasure, 2009, 32(6):4.
[5]
Xu M, Li B H, Wang K, Research on individual identification method of specific emitter [J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(10):116-123.
[6]
Pu Y W, Guo J, Liu T T, Radar emitter signal recognition based on ambiguity function contour lines and stacked denoising auto-encoders [J]. Chinese Journal of Scientific Instrument, 2021, 42(1): 207-216.
[7]
Wang L. Research on radar signal recognition based on neural network [D]. Harbin Engineering University, 2020.
[8]
Chen X P, Ou W J, Zhang Z. Radar emitter identification based on LightGBM and feature engineering [J]. Electronic Information Warfare Technology, 2021, 36(5): 54-58.
[9]
Wang X F, Dong H X, Yu Y, Radar signal recognition method based on improved RBF neural network [J]. Foreign Electronic Measurement Technology, 2022, 41(5):5.
[10]
Niu H N, Wang W C, Liu Q B. Radar radiation source identification based on convolution neural network [J]. Modern Defence Technology, 2021, 49(3):130-136.
[11]
Zhang M. Design of efficient neural network for radar signal recognition [D]. Nanjing University of Posts and Telecommunications, 2022.
[12]
Xiao L Z, Huang H, Zhang G Y. Radar emitter recognition technology based on fuzzy matching and neural network [J]. Shipboard Electronic Countermeasure, 2009,32(04):57-62.
[13]
Sun H. Research on fingerprint features extraction and identification method of radar emitter signal [D]. Beijing University of Posts and Telecommunications, 2020.
[14]
Zhang G, Rong H, Jin W, Radar emitter signal recognition based on resemblance coefficient features [C]. International Conference on Rough Sets and Current Trends in Computing, 2004: 665-670.
[15]
Zhang W X, Sun F L, Wang B, Radar signal intra-pulse feature extraction based on improved wavelet transform algorithm[J]. International Journal of Communications, Network and System Sciences, 2017, 10(8):118-127.
[16]
Kang N X, He M H, Han J, Radar signal recognition method via synthetic analysis in time-frequency domain [J]. Modern Defence Technology, 2017, 045(005):162-169.
[17]
Guo L, Chen X. Low probability of intercept radar signal recognition based on the improved AlexNet model [C]. Proceedings of 2nd International Conference on Digital Signal Processing, 2018:120-125.
[18]
Yang J, Ge J D. Radar signal recognition based on BP neural network optimized by improved GA [J]. Journal of Xi'an University of Posts and Telecommunications, 2019, 24(6):5.
[19]
Cai J, Li C, Zhang H. Modulation recognition of radar signal based on an improved CNN model [C]. IEEE 7th International Conference on Computer Science and Network Technology, 2019:293-297.
[20]
Lin C L, Chen T P, Fan K C, Radar high-resolution range profile ship recognition using two-channel convolutional neural networks concatenated with bidirectional long short-term memory [J]. Remote Sensing, 2021, 13(7): 1259.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ISIA '23: Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation
December 2023
292 pages
ISBN:9798400709401
DOI:10.1145/3632314
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 December 2023

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Advantages
  2. Belt Conveyor
  3. Features
  4. Tension Device

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ISIA 2023

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 21
    Total Downloads
  • Downloads (Last 12 months)16
  • Downloads (Last 6 weeks)2
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media