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Alexander Totsky
Researcher at National Aerospace University – Kharkiv Aviation Institute
Publications - 45
Citations - 379
Alexander Totsky is an academic researcher from National Aerospace University – Kharkiv Aviation Institute. The author has contributed to research in topics: Bispectrum & Radar. The author has an hindex of 10, co-authored 40 publications receiving 330 citations. Previous affiliations of Alexander Totsky include National Academy of Sciences of Ukraine.
Papers
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Journal ArticleDOI
Classification of aircraft using micro-Doppler bicoherence-based features
Pavlo Molchanov,Karen Egiazarian,Jaakko Astola,Alexander Totsky,S.P. Leshchenko,Maria-Pilar Jarabo-Amores +5 more
TL;DR: A novel bicoherence-based method is proposed for the classification of aerial radar targets in automatic target recognition (ATR) systems based on classification features computed in the form of bICOherence estimates, as well as cepstral coefficients extracted from the micro-Doppler contribution contained in radar returns.
Proceedings ArticleDOI
Classification of ground moving radar targets by using joint time-frequency analysis
TL;DR: Two novel algorithms for ground moving target classification using additional information features related to the radial velocity variability are proposed and studied and compared with feature extraction method based on linear prediction model and cepstrum coefficients.
Journal ArticleDOI
Application of Bispectrum Estimation for Time-Frequency Analysis of Ground Surveillance Doppler Radar Echo Signals
Jaakko Astola,Karen Egiazarian,G.I. Khlopov,S.I. Khomenko,I.V. Kurbatov,V.Ye. Morozov,Alexander Totsky +6 more
TL;DR: A microwave coherent homodyne and polarimetric ground surveillance Doppler radar is employed for collecting the radar returns from moving objects and a clean recovery of evolutionary phase-coupled harmonics for such targets as a swinging metallic sphere or a walking human is demonstrated.
Proceedings ArticleDOI
Ground moving target classification by using DCT coefficients extracted from micro-Doppler radar signatures and artificial neuron network
TL;DR: A novel approach to ground moving targets classification by using information features contained in micro-Doppler radar signatures by using discrete cosine transform coefficients extracted from radar signature and multilayer perceptron (MLP) as a classifier is presented.
Journal ArticleDOI
Classification of ground moving targets using bicepstrum-based features extracted from Micro-Doppler radar signatures
TL;DR: Experimental real radar measurements demonstrated that it is quite feasible to discern three classes of humans walking in a vegetation cluttered environment using proposed bicepstrum-based classification features.