Ground moving target recognition using log energy entropy of wavelet packets

GÖKSU H.

ELECTRONICS LETTERS, cilt.54, sa.4, ss.233-235, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 54 Sayı: 4
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1049/el.2017.4267
  • Dergi Adı: ELECTRONICS LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.233-235
  • Anahtar Kelimeler: entropy, wavelet transforms, search radar, radar target recognition, Doppler radar, time-frequency analysis, radar resolution, signal representation, multilayer perceptrons, signal classification, radar computing, ground moving target recognition, wavelet packet log energy entropy, ground surveillance radar automatic target recognition, RF pulsed Doppler radar, WPA, arbitrary time-frequency resolution, stationary signal analysis, nonstationary signal analysis, time representation, high-frequency resolution, multilayer perceptron, hierarchical scheme, binary classification steps, CLASSIFICATION
  • Akdeniz Üniversitesi Adresli: Evet

Özet

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.