Bird identification on 1290-MHz wind profiler data applying neural networks and neurofuzzy systems

Abstract

Migrating birds can severely affect data from wind profilers operating in the 1000 MHz range. Recent methods for removing bird contamination do not seem to solve the problem satisfactorily. Here, a new method, the Quantum Neurofuzzy Bird Identification and Removal Deck (NEURO-BIRD) is presented. The algorithm has an overall classification rate of over 90 % for birds, clear air returns, and rain echoes for single, one-second wind profiler spectra. Even with very heavy migration, high quality hourly winds can be obtained. Because the source of contamination of the spectra is unambiguously identified, bird data can be supplied for ornithological research. NEURO-BIRD is very fast and well suited for real-time applications.