http://iet.metastore.ingenta.com
1887

Radar emitter classification based on unidimensional convolutional neural network

Radar emitter classification based on unidimensional convolutional neural network

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Radar, Sonar & Navigation — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Radar emitter classification (REC) is an essential part of electronic warfare (EW) systems. In REC tasks, after deinterleaving, the intercepted radar signals are classified into specific radar types. With new radar types arising and the electromagnetism environment getting complicated, REC has become a big data problem. Meanwhile, there exist inconsistent features among samples. These two problems can affect the performance of classification. In this work, first, the authors designed a novel encoding method to deal with the inconsistent features. High-dimension sequences of equal length are generated as new features. Then a deep learning model named unidimensional convolutional neural network (U-CNN) is proposed to classify the encoded high-dimension sequences with big data. A large and complex radar emitter's dataset is used to evaluate the performance of the U-CNN model with the encoding method. Experiments show that the authors' proposal gains an improvement of 2–3% in accuracy compared with the state-of-the-art methods, while the time consumed for identifying 45,509 emitters is only 1.95 s using a GPU. Specifically, for 12 indistinguishable radars, the classification accuracy is improved about 15%.

References

    1. 1)
      • 1. Spezio, A.E.: ‘Electronic warfare systems’, IEEE Trans. Microw. Theory Tech., 2002, 50, (3), pp. 633644.
    2. 2)
      • 2. Matuszewski, J., Paradowski, L.: ‘The knowledge based approach for emitter identification’. 12th Int. Conf. on Microwaves and Radar, Krakow, Poland, 1998, pp. 810814.
    3. 3)
      • 3. Zhang, G., Jin, W., Hu, L.: ‘Radar emitter signal recognition based on support vector machines’. 8th Proc. Int. Conf. on Control, Automation, Robotics and Vision, Kunming, China, 2004, pp. 826831.
    4. 4)
      • 4. Jordanov, I., Petrov, N., Petrozziello, A.: ‘Supervised radar signal classification’. Int. Joint Conf. on Neural Networks, Vancouver, Canada, July 2016, pp. 14641471.
    5. 5)
      • 5. Zhu, W., Li, M., Zeng, C.: ‘Research on online learning of radar emitter recognition based on hull vector’. IEEE Second Int. Conf. on Data Science in Cyberspace, Shenzhen, China, June 2017, pp. 328332.
    6. 6)
      • 6. Xu, J., He, M., Han, J., et al: ‘A comprehensive estimation method for kernel function of radar signal classifier’, Chin. J. Electron., 2015, 24, (1), pp. 218222.
    7. 7)
      • 7. LeCun, Y.L., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436444.
    8. 8)
      • 8. Nedyalko, P., Jordanov, I., Roe, J.: ‘Radar emitter signals recognition and classification with feedforward networks’, Procedia Comput. Sci., 2013, 22, (22), pp. 11921200.
    9. 9)
      • 9. Zhang, Z., Guan, X., He, Y.: ‘Study on radar emitter recognition signal based on rough sets and RBF neural network’, Proc. Int. Conf. on Machine Learning and Cybernetics, Baoding, China, July 2009, pp. 12251230.
    10. 10)
      • 10. Kim, L.S., Bae, H.B., Kil, R.M., et al: ‘Classification of the trained and untrained emitter types based on class probability output networks’, Neurocomputing, 2017, 248, (2017), pp. 6775.
    11. 11)
      • 11. Yang, Z., Wu, Z., Yin, Z., et al: ‘Hybrid radar emitter recognition based on rough k-means classifier and relevance vector machine’, Sensors, 2013, 13, (2013), pp. 848864.
    12. 12)
      • 12. Chen, W., Fu, K., Zuo, J, et al: ‘Radar emitter classification for large data set based on weighted-xgboost’, IET Radar Sonar Navig., 2017, 11, (8), pp. 12031207.
    13. 13)
      • 13. Matuszewski, J.: ‘Applying the decision trees to radar targets recognition’. 11th Int. Radar Symp., Vilnius, Lithuania, June 2010, pp. 14E.
    14. 14)
      • 14. Ata'a, A., Abdullah, S.: ‘Deinterleaving of radar signals and PRF identification algorithms’, IET Radar Sonar Navig., 2016, 1, (5), pp. 340347.
    15. 15)
      • 15. Shieh, C., Lin, C.: ‘A vector neural network for emitter identification’, IEEE Trans. Antennas Propag., 2002, 50, (8), pp. 11201127.
    16. 16)
      • 16. Liu, H., Liu, Z., Jiang, W., et al: ‘Approach based on combination of vector neural networks for emitter identification’, IET Signal Process., 2010, 4, (2), pp. 137148.
    17. 17)
      • 17. Lecun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 22782324.
    18. 18)
      • 18. Alex, K., Sutskever, I., Hinton, G.: ‘Imagenet classification with deep convolutional neural networks’. Proc. Int. Conf. on Neural Information Processing Systems, Lake Tahoe, USA, December 2012, pp. 10971105.
    19. 19)
      • 19. Glorot, X., Bordes, A., Bengio, Y.: ‘Deep sparse rectifier neural networks’. Proc. of the 14th Int. Conf. on Artificial Intelligence and Statistics, Fort Lauderdale, USA, April 2011, pp. 315323.
    20. 20)
      • 20. Bouvrie, J.: ‘Notes on convolutional neural networks’. Technical report, 2006.
    21. 21)
      • 21. Russakovsky, O., Deng, J., Su, H., et al: ‘Imagenet large scale visual recognition challenge’, Int. J. Comput. Vis., 2015, 115, (3), pp. 211252.
    22. 22)
      • 22. TensorFlow, , https://www.tensorflow.org/, accessed 2 November 2017.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2017.0547
Loading

Related content

content/journals/10.1049/iet-rsn.2017.0547
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address