Micro-Doppler-based classification study on the detections of aerial targets and wind turbines

Abstract

In this study, micro-Doppler-based aerial target classification is examined together with the consideration of wind turbine clutter (WTC). In the examinations, wavelet coefficients extracted from micro-Doppler profiles are employed as classifier features for the airliner-, glider- and helicopter-type aerial targets and also for the examined wind turbine (WT) model. In order to simulate the targets' scatterings more accurately, their computer-aided design (CAD) models are considered. Moreover, scattering characteristics of the targets are taken into account for a variety of radar aspects and propeller or blade rotation speeds. Through the simulation results obtained by employing Bayesian and probabilistic neural network (PNN) classifiers, classification performance of a typical air traffic control (ATC) radar system is exhibited. Additionally, the results present the recognisability of WTC on ATC systems via the classification procedure.