A novel hybrid model for inversion problem of atmospheric refractivity estimation

Tepecik C., NAVRUZ İ.

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, cilt.84, ss.258-264, 2018 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 84
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.aeue.2017.12.009
  • Dergi Adı: AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.258-264
  • Anahtar Kelimeler: Atmospheric refractivity estimation, Radio wave propagation, Propagation factor, Artificial neural networks, Genetic algorithms, Hybrid model, EVAPORATION DUCT HEIGHTS, RADIO REFRACTIVITY, RADAR, PROPAGATION
  • Ankara Üniversitesi Adresli: Evet

Özet

Atmospheric refractivity estimation is an important issue for performance evaluation of communication systems and air surveillance radars. A novel hybrid model based on artificial neural networks (ANNs) and genetic algorithms (GAs) for inversion problem of atmospheric refractivity estimation is introduced. In this paper, inversion problem and clutter model problem of refractivity from clutter (RFC) method are separated and only inversion problem is studied. A problem specific ANN structure is designed and an original GA is developed to fulfill atmospheric refractivity estimations. In hybrid method, ANNs make pre-estimation and GAs use these results as a starting population for post-estimation. When the results obtained from the single solutions of ANNs and GAs are compared to the results obtained from hybrid model, a significant improvement in the accuracy of estimated results is observed.