Automatic target recognition of ground surveillance radars is a beneficial practice that reduces cost, error, duration of detection and response time. The identification of echoes from eight classes using an RF pulsed Doppler radar is studied. Instead of traditional methods, the wavelet packet analysis (WPA) is used. WPA, by providing arbitrary time–frequency resolution, enables analysing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. WPA subimages are further analysed to obtain feature vectors of log energy entropy in all cases except one case where norm entropy is used. These features are fed into a multilayer perceptron for classification which is done in a hierarchical scheme and composed of binary classification steps. Hundred per cent accuracy was possible in all steps except two which were realised at 99.5 and 98.2%. An overall classification rate of 98.2% was achieved. The results were compared with other published results that report accuracy in the same application field. The proposed method achieved the highest accuracy although it had the highest number of classes. The method provides a promising tool for the automatic recognition of ground surveillance radar targets.

References

1. 1)
  - 9. Smith, G., Woodbridge, K., Baker, C.: ‘Radar micro-Doppler signature classification using dynamic time warping’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (3), pp. 1078–1096 (doi: 10.1109/TAES.2010.5545175).
2. 2)
  - 13. Roth, M.W.: ‘Survey of neural network technology for automatic target recognition’, Trans. Neural Netw., 1990, 1, (1), pp. 28–43 (doi: 10.1109/72.80203).
3. 3)
  - 11. Wu, J.D., Liu, C.H.: ‘An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network’, Expert Syst. Appl., 2009, 36, (3), pp. 4278–4286 (doi: 10.1016/j.eswa.2008.03.008).
4. 4)
  - 8. The Database of Radar Echoes from Various Targets, http://cid-3aaf3e18829259c0.skydrive.live.com/home.aspx, accessed 03.08.2017.
5. 5)
  - 10. Yen, G.G., Lin, K.C.: ‘Wavelet packet feature extraction for vibration monitoring’, Trans. Ind. Electron., 2000, 47, (3), pp. 650–667 (doi: 10.1109/41.847906).
6. 6)
  - 12. Akansu, A.N., Haddad, R.A.: ‘Multiresolution signal decomposition: transforms, subbands, and wavelets’ (Acad. Press, San Diego, CA, USA, 2001).
7. 7)
  - 6. Andrić, M. S., Bondžulić, B.P., Zrnić, B.M.: ‘The database of radar echoes from various targets with spectral analysis’. Neural Network Applications in Electrical Engineering (NEUREL), 10th Symp. on, IEEE, September 2010, pp. 187–190).
8. 8)
  - 15. Jahangir, M., Ponting, K.M., O'loghlen, J.W.: ‘A robust Doppler classification technique based on hidden Markov models’, IEE Proceedings-Radar, Sonar and Navigation, 2003, 150, (1), pp. 33–36 (doi: 10.1049/ip-rsn:20030027).
9. 9)
  - 4. Molchanov, P., Astola, J., Egiazarian, K., et al: ‘Ground moving target classification by using DCT coefficients extracted from micro-Doppler radar signatures and artificial neuron network’. In Microwaves, Radar and Remote Sensing Symp. (MRRS), IEEE, August 2011, pp. 173–176.
10. 10)
  - 3. Eryildirim, A., Onaran, I.: ‘Pulse Doppler radar target recognition using a two-stage SVM procedure’, Trans. Aerosp. Electron. Syst., 2011, 47, (2), pp. 1450–1457 (doi: 10.1109/TAES.2011.5751269).
11. 11)
  - 8. Bilik, I., Tabrikian, J., Cohen, A.: ‘GMM-based target classification for ground surveillance Doppler radar’, IEEE Trans. Aerosp. Electron. Syst., 2006, 42, (1), pp. 267–278 (doi: 10.1109/TAES.2006.1603422).
12. 12)
  - 9. Wickerhauser, M.V.: ‘Adapted wavelet analysis from theory to software’ (IEEE Press, Piscataway, NJ, USA, 1994).
13. 13)
  - 14. Lippmann, R.P.: ‘Pattern classification using neural networks’, Commun. Mag., 1989, 27, (11), pp. 47–50 (doi: 10.1109/35.41401).
14. 14)
  - 7. Andrić, M.S., Bondžulić, B.P., Zrnić, B.M.: ‘Feature extraction related to target classification for a radar Doppler echoes’. Proc. of the 18th Telecommunications forum, November 2010, pp. 725–728.
15. 15)
  - 13. Liao, X., Runkle, P., Carin, L.: ‘Identification of ground targets from sequential high-range-resolution radar signatures’, IEEE Trans. Aerosp. Electron. Syst., 2002, 38, (4), pp. 1230–1242 (doi: 10.1109/TAES.2002.1145746).

Ground moving target recognition using log energy entropy of wavelet packets

References

Related content