Vol. 109

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Towards Localization and Classification of Birds and Bats in Windparks Using Multiple FMCW-Radars at Ka-Band

By Ashkan Taremi Zadeh, Murat Diyap, Jochen Moll, and Viktor Krozer
Progress In Electromagnetics Research M, Vol. 109, 1-12, 2022


Birds and bats are at risk when they are flying near wind turbines (WT). Hence, a protection of bats and birds is postulated to reduce their mortality e.g. due to collisions with the rotor-blades. The use of radar technology for monitoring wind energy installations is becoming increasingly attractive for WT operators, as it offers many advantages over other sensor systems. Timely localization and classification of the approaching animal species is very crucial about the reaction measures for collision avoidance. In this work, a localization, classification and flight path prediction technique has been developed and tested based on simulated radar signals. This allowed us to classify three different birds and one bat species with an accuracy of 90.18%. For accurate localization and target tracking, five frequency modulated continuous wave (FMCW) radars operating in Ka-Band were placed on the tower of the WT for 360˚ monitoring of the WT.


Ashkan Taremi Zadeh, Murat Diyap, Jochen Moll, and Viktor Krozer, "Towards Localization and Classification of Birds and Bats in Windparks Using Multiple FMCW-Radars at Ka-Band," Progress In Electromagnetics Research M, Vol. 109, 1-12, 2022.


    1. Brinkmann, R., O. Behr, I. Niermann, and M. Reich (eds.), Entwicklung von Methoden zur Unter-suchung und Reduktion des Kollisionsrisikos von Fledermäusen an Onshore-Windenergieanlagen: Ergebnisse eines Forschungsvorhabens, Umwelt und Raum, Schriftenreihe Institut für Umweltplanung, Cuvillier-Verl., Göttingen, 2011.

    2. Saidur, R., N. A. Rahim, M. R. Islam, and K. H. Solangi, "Environmental impact of wind energy," Renewable and Sustainable Energy Reviews, Vol. 15, No. 5, 2423-2430, June 2011.

    3. Rydell, J., H. Engström, A. Hedenström, J. K. Larsen, J. Pettersson, and M. Green, The Effect of Wind Power on Birds and Bats - A Synthesis, 6511, Swedish Environmental Protection Agency, 2012.

    4. Grünkorn, T., J. Blew, T. Coppack, O. Krüger, G. Nehls, A. Potiek, M. Reichenbach, J. von Rönn, H. Timmermann, and S.Weitekamp, Ermittlung Der Kollisionsraten von (Greif) Vögeln Und Schaffung Planungsbezogener Grundlagen Für Die Prognose Und Bewertung Des Kollisionsrisikos Durch Windenergieanlagen (PROGRESS). Schlussbericht Zum Durch Das Bundesministerium Für Wirtschaft Und Energie (BMWi) Im Rahmen Des 6. Energieforschungsprogrammes Der Bundesregierung Geförderten Verbundvorhaben PROGRESS, FKZ 0325300A-D, 2016.

    5. Bulling, L., D. Sudhaus, D. Schnittker, E. Schuster, J. Biehl, and F. Tucci, Vermeidungsma-maβnahmen Bei Der Planung Und Genehmigung von Windenergieanlagen - Bundesweiter Katalog von Maβnahmen Zur Verhinderung Des Eintritts von Artenschutzrechtlichen Verbotstatbeständen Nach s 44 BNatSchG, Fachagentur Windenergie an Land, 2015.

    6. Mao, X., J. K. Chow, P. S. Tan, K.-F. Liu, J. Wu, Z. Su, Y. H. Cheong, G. L. Ooi, C. C. Pang, and Y.-H. Wang, "Domain randomization-enhanced deep learning models for bird detection," Scientific Reports, Vol. 11, No. 1, 639, December 2021.

    7. Niemi, J. and J. T. Tanttu, "Deep learning-based automatic bird identification system for offshore wind farms," Wind Energy, Vol. 23, No. 6, 1394-1407, 2020.

    8. McClure, C. J. W., B. W. Rolek, L. Dunn, J. D. McCabe, L. Martinson, and T. Katzner, "Eagle fatalities are reduced by automated curtailment of wind turbines," Journal of Applied Ecology, Vol. 58, No. 3, 446-452, 2021.

    9. Linder, A. C., H. Lyhne, B. Laubek, D. Bruhn, and C. Pertoldi, "Quantifying raptors' flight behavior to assess collision risk and avoidance behavior to wind turbines," Preprints, 2021, doi: 10.20944/preprints202102.0391.v1.

    10. Rahman, S. and D. A. Robertson, "Classification of drones and birds using convolutional neural networks applied to radar micro-doppler spectrogram images," IET Radar, Sonar and Navigation, Vol. 14, No. 5, 653-661, 2020.

    11. Björklund, S. and N. Wadströmer, "Target detection and classification of small drones by deep learning on radar micro-doppler," 2019 International Radar Conference (RADAR), 1-6, 2019.

    12. Li, D., R. Chen, J. Gong, and J. Yan, "Comparison of radar signatures based on flight morphology for large birds and small birds," IET Radar, Sonar and Navigation, Vol. 14, No. 4, 1365-1369, September 2020.

    13. Zaugg, S., G. Saporta, E. van Loon, H. Schmaljohann, and F. Liechti, "Automatic identification of bird targets with radar via patterns produced by wing apping," Journal of The Royal Society Interface, Vol. 5, No. 26, 1041-1053, September 2008.

    14. Zadeh, A. T., M. Mälzer, D. H. Nguyen, J. Moll, and V. Krozer, "Radar-based detection of birds at wind turbines: Numerical analysis for optimum coverage," 2021 15th European Conference on Antennas and Propagation (EuCAP), 1-5, 2021.

    15. Nguyen, D. H., J. Ala-Laurinaho, J. Moll, V. Krozer, and G. Zimmer, "Improved sidelobe suppression microstrip patch antenna array by uniform feeding networks," IEEE Transactions on Antennas and Propagation, 2020.

    16. Lipa, B. J. and D. E. Barrick, "FMCW signal processing,", 1990.

    17. Balanis, C. A., Advanced Engineering Electromagnetics, 2nd Ed., John Wiley & Sons Inc., 2012.

    18. Cumming, I. G. and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithm and Implementation, Artech House Publishers, 2005.

    19. Lacomme, P., J.-P. Hardange, J.-C. Marchais, and E. Normant, "Noise and spurious signals," Air and Spaceborne Radar Systems, 47-58, 2001.

    20. Crecraft, D. I. and S. Gergely, Analog Electronics, 2002.

    21. Scherr, S., R. Afroz, S. Ayhan, S. Thomas, T. Jaeschke, S. Marahrens, A. Bhutani, M. Pauli, N. Pohl, and T. Zwick, "Influence of radar targets on the accuracy of FMCW radar distance measurements," IEEE Transactions on Microwave Theory and Techniques, Vol. 65, No. 10, 3640-3647, 2017.

    22. Dakin, B. G. R., "The biophysics of bird ight: Functional relationships integrate aerodynamics, morphology, kinematics, muscles, and sensors," Canadian Journal of Zoology, Vol. 93, No. 12, 964, 2015.

    23. Rahman, S. and D. A. Robertson, "In-flight RCS measurements of drones and birds at K-band and W-band," IET Radar, Sonar & Navigation, Vol. 13, No. 2, 300-309, 2019.

    24. Urmy, S. S. and J. D. Warren, "Quantitative ornithology with a commercial marine radar: Standard-target calibration, target detection and tracking, and measurement of echoes from individuals and ocks," Methods in Ecology and Evolution, Vol. 8, No. 7, 860-869, November 2016.

    25. Jahangir, M., B. I. Ahmad, and C. J. Baker, "Robust drone classification using two-stage decision trees and results from sesar safir trials," 2020 IEEE International Radar Conference (RADAR), 636-641, 2020.

    26. Bruderer, B., D. Peter, A. Boldt, and F. Liechti, "Wing-beat characteristics of birds recorded with tracking radar and cine camera," Ibis, Vol. 152, No. 2, 272-291, April 2010.

    27. Taylor, L. A., G. K. Taylor, B. Lambert, J. A. Walker, D. Biro, and S. J. Portugal, "Birds invest wingbeats to keep a steady head and reap the ultimate benefits of ying together," PLOS Biology, Vol. 17, No. 6, e3000299, June 2019.

    28. Mirkovic, D., P. M. Stepanian, J. F. Kelly, and P. B. Chilson, "Electromagnetic model reliably predicts radar scattering characteristics of airborne organisms," Scientific Reports, Vol. 6, No. 1, 35637, December 2016.

    29. Bruderer, B. and A. G. Popa-Lisseanu, "Radar data on wing-beat frequencies and ight speeds of two bat species," Acta Chiropterologica, Vol. 7, No. 1, 73-82, June 2005.

    30. Ostertagová, E., "Modelling using polynomial regression," Procedia Engineering, Vol. 48, 500-506, Modelling of Mechanical and Mechatronics Systems, 2012.