Publisher DOI: 10.23919/IRS.2017.8008217
Title: Data mining classification of cars based on GPS shadows in Forward Scatter Radar systems
Language: English
Authors: Kabakchiev, Christo 
Kabakchieva, Dorina 
Garvanov, Iwan 
Behar, Vera 
Kabakchiev, Kalin 
Rohling, Hermann 
Kulpa, Krzysztof 
Yarovoy, Alexander 
Issue Date: 10-Aug-2017
Source: Proceedings International Radar Symposium : 8008217 (2017-08-10)
Journal or Series Name: Proceedings International Radar Symposium 
Abstract (english): The goal of this paper is to introduce a new concept - using Data Mining approach for radar classification of vehicles, based on their GPS shadow signals detected in a GPS L1-based Forward Scatter Radar (FSR) system. Real data is used for the current experiments, recording the GPS radio shadows of several moving vehicles with commercial non-professional GPS equipment. The records are further processed in MATLAB computational environment in order to obtain the estimated parameters of the GPS shadows. Data Mining approach is then implemented for classifying the cars, comparing classifiers generated with different classification algorithms -A decision tree, a neural network, and a Bayesian classifier, for two different variants of the class variable, taking three or two possible values. The best results are achieved for the two values of the class variable and the best performing classifiers are the neural networks..
URI: http://hdl.handle.net/11420/3254
ISBN: 978-373699343-3
ISSN: 2155-5753
Institute: Nachrichtentechnik E-8 
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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