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

Convolutional neural network for classifying space target of the same shape by using RCS time series

Convolutional neural network for classifying space target of the same shape by using RCS time series

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.

Warhead and decoy classification is one of the most important and difficult technical problems in ballistic missile defence. The conventional methods extract features from the measured data and employ some classification algorithms. However, it is hard to extract all the information embedded in the raw data, and there might be contradictory features lowering the classification ability. A one-dimensional convolutional neural network structure named RCSnet was proposed to classify the warhead and decoy targets of the same shape in midcourse, which directly utilises the radar cross-section (RCS) time series. It was compared with 5 conventional classification algorithms which used 26 selected features on simulation dataset, and it outperformed them in both classification performance and predicting speed. Different training algorithms and networks of the RCSnet structure with different filter numbers were explored for better utilising the RCSnet.

References

    1. 1)
      • 1. Liu, L.: ‘Ballistic missile micro-Doppler feature extraction based on radar measurements’. PhD thesis, National University of Defense Technology, 2012.
    2. 2)
      • 2. Cepek, R.J.: ‘Ground-based midcourse defense: continue testing, but operational fielding must take a backseat to theater missile defense and homeland security’. Master thesis, Joint Forces Staff College, Joint Advanced Warfighting School, 2005.
    3. 3)
      • 3. Lemnios, W.Z., Grometstein, A.A.: ‘Overview of the Lincoln laboratory ballistic missile defense program’, Linc. Lab. J., 2002, 13, pp. 932.
    4. 4)
      • 4. Wang, W., Chen, L., Lei, Y.: ‘Micro-motion analysis of decoy in midcourse of ballistic missile’, Syst. Eng. Electron., 2016, 38, pp. 487492.
    5. 5)
      • 5. Chen, J., Xu, S., Tian, B., et al: ‘A fast recognition method of warhead target in boost phase using kinematic features’. MIPPR 2015, Enshi, China, 2015.
    6. 6)
      • 6. Persico, A., Clemente, C., Pallotta, L., et al: ‘Micro-Doppler classification of ballistic threats using krawtchouk moments’. IEEE Radar Conf., Philadelphia, USA, 2016.
    7. 7)
      • 7. Zhang, H., Ding, D., Fan, Z., et al: ‘Adaptive neighborhood-preserving discriminant projection method for HRRP-based radar target recognition’, IEEE Antennas Wirel. Propag. Lett., 2015, 14, pp. 650653.
    8. 8)
      • 8. Feng, B., Chen, B., Liu, H.: ‘Radar HRRP target recognition with deep networks’, Pattern Recognit., 2017, 61, pp. 379393.
    9. 9)
      • 9. Jiang, Y., Li, Y., Cai, J., et al: ‘Robust automatic target recognition via HRRP sequence based on scatterer matching’, Sensors(Basel), 2018, 2, pp. 593611.
    10. 10)
      • 10. Housseini, A.E., Toumi, A., Khenchaf, A.: ‘Deep learning for target recognition from SAR images’. Detection Systems Architectures and Technologies, Algiers, Algeria, 2017.
    11. 11)
      • 11. Lazarov, A., Minchev, C.: ‘ISAR image recognition algorithm and neural network implementation’, Cybern. Inf. Technol., 2017, 17, (4), pp. 183199.
    12. 12)
      • 12. Xu, X., Huang, P.: ‘Radar target recognition using RCS magnitude signatures’, Syst. Eng. Electron., 1992, 6, pp. 19.
    13. 13)
      • 13. Lei, X., Fu, X., Wang, C., et al: ‘Statistical feature selection of narrowband RCS sequence based on greedy algorithm’. IEEE Cie Int. Conf. Radar, Chengdu, China, 2012, pp. 16641667.
    14. 14)
      • 14. Xiang, X., Xu, X.: ‘Feature extraction for radar target recognition using time sequences of radar cross section measurements’. Int. Congress Image and Signal Processing, Hangzhou, China, 2014, pp. 15831587.
    15. 15)
      • 15. Delisle, G.Y., Zebbani, Z., Charrier, C., et al: ‘A novel approach to complex target recognition using RCS wavelet decomposition’, IEEE. Antennas. Propag. Mag., 2005, 47, pp. 3555.
    16. 16)
      • 16. Wang, T., Bi, W., Zhao, Y., et al: ‘Target recognition algorithm based on RCS observation sequence – Set-valued identification method’. Control Conf., Nanjing, China, 2014, pp. 881886.
    17. 17)
      • 17. Wei, W., Cai, H.: ‘Narrowband radar ballistic missile target recognition technology based on SVM’, Electron. Sci. Technol., 2016, 29, pp. 7578.
    18. 18)
      • 18. Liu, L., Wang, Z., Hu, W.: ‘Precession period extraction of ballistic missile based on radar measurement’. Int. Conf. Radar, Shanghai, China, 2006, pp. 14.
    19. 19)
      • 19. Feng, D., Liu, J., Dan, M.: ‘RCS periodicity of ballistic target in midcourse and its estimation algorithms’, J. Astronaut., 2008, 29, pp. 362365.
    20. 20)
      • 20. Zhang, S.: ‘Procession period estimation of RCS sequences based on trigonometric function fitting’, J. Electron. Inf. Technol., 2014, 36, pp. 13891393.
    21. 21)
      • 21. Lee, K.C., Ou, J.S.: ‘Radar target recognition by using linear discriminant algorithm on angular-diversity RCS’, J. Electromagn. Waves Appl., 2007, 21, pp. 20332048.
    22. 22)
      • 22. Huang, C.W., Lee, K.C.: ‘Application of ICA technique to PCA based radar target recognition’, Prog. Electromagn. Res., 2010, 105, pp. 157170.
    23. 23)
      • 23. Chan, S.C., Lee, K.C.: ‘Radar target identification by kernel principal component analysis on RCS’, J. Electromagn. Waves Appl., 2012, 26, pp. 6474.
    24. 24)
      • 24. Chan, S.C., Lee, K.C.: ‘Radar target recognition by MSD algorithms on angular-diversity RCS’, IEEE Antennas Wirel. Propag. Lett., 2013, 12, pp. 937940.
    25. 25)
      • 25. Chan, S.C., Lee, K.C.: ‘Angular-diversity target recognition by kernel scatter-difference based discriminant analysis on RCS’, Int. J. Appl. Electromagn. Mech., 2013, 42, pp. 409420.
    26. 26)
      • 26. Zhao, A., Niu, W., Guo, L.: ‘Recognition techniques and feature extraction for space targets based on RCS’. Control Conf. 2008 CCC, Kumming, China, 2008, pp. 426429.
    27. 27)
      • 27. Wang, C., Zhang, S.: ‘Target recognition of ballistic RCS data based on binary tree SVM’, Mod. Rad., 2015, 37, pp. 2527.
    28. 28)
      • 28. Xia, J.: ‘Research on the application of deep learning network to radar target recognition technique’. Master thesis, National University of Defense Technology, 2016.
    29. 29)
      • 29. Chen, B., Tong, C., Li, X.: ‘Target recognition of the typical aircraft based on the dynamic RCS’, Mod. Rad., 2017, 1, pp. 2631.
    30. 30)
      • 30. Breiman, L., Friedman, J., Olshen, R., et al: ‘Classification and regression trees’ (CRC Press, Boca Raton, FL, 1984).
    31. 31)
      • 31. Maas, A.L., Hannun, A.Y., Ng, A.Y.: ‘Rectifier nonlinearities improve neural network acoustic models’, 2013.
    32. 32)
      • 32. Ioffe, S., Szegedy, C.: ‘Batch normalization: accelerating deep network training by reducing internal covariate shift’, Comput. Sci., 2015, pp. 111, Article ID: arXiv:1502.03167.
    33. 33)
      • 33. Srivastava, N., Hinton, G., Krizhevsky, A., et al: ‘Dropout: a simple way to prevent neural networks from overfitting’, J. Mach. Learn. Res., 2014, 15, pp. 19291958.
    34. 34)
      • 34. Sutskever, I., Martens, J., Dahl, G., et al: ‘On the importance of initialization and momentum in deep learning’. Int. Conf. Machine Learning, Atlanta, USA, 2013, pp. 11261139.
    35. 35)
      • 35. Duchi, J., Hazan, E., Singer, Y.: ‘Adaptive subgradient methods for online learning and stochastic optimization’, J. Mach. Learn. Res., 2011, 12, pp. 21212159.
    36. 36)
      • 36. Zeiler, M.D.: ‘ADADELTA: an adaptive learning rate method’, Comput. Sci., 2012, pp. 16, Article ID: arXiv:1212.5701.
    37. 37)
      • 37. Kingma, D.P., Ba, J.: ‘Adam: a method for stochastic optimization’, Comput. Sci., 2014, pp. 113, Article ID: arXiv:1412.6980.
    38. 38)
      • 38. ‘FEKO - EM Simulation Software’, http://www.feko.info/, accessed 5 March 2018.
    39. 39)
      • 39. ‘Runge-Kutta methods’, https://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods, accessed 5 March 2018.
    40. 40)
      • 40. Zhang, F., Tian, K., Xi, M.: ‘The study on ballistic missile modeling and tracking’, J. Projectiles, Rockets, Missiles Guid., 2012, 32, pp. 5358.
    41. 41)
      • 41. Chollet, F.: ‘Keras’, GitHub, https://github.com/keras-team/keras, 2015.
    42. 42)
      • 42. Abadi, M., Agarwal, A., Barham, P., et al: ‘Tensorflow: large-scale machine learning on heterogeneous systems’, Software available from tensorflow.org, 2015.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2018.5237
Loading

Related content

content/journals/10.1049/iet-rsn.2018.5237
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address