000			02040naa a22003138i 4500
999			_c5324 _d5324
001			00005324
003			FR-PaOEC
005			20211006062617.0
006			a o d i
007			cr || |||m|n||
008			190601s2018 ||| o i|0| 0 eng d
035			_a(FR-PaOEC)
040			_aFR-PaOEC _cES-MaONT
100	1		_aMartín, Juan Pablo _93357
245	1	0	_aOpportunities and challenges of global flight data acquisition /​ _cJuan Pablo Martín and Martín Gabriel Riolfo
260			_aGeneva : _bInternational Telecommunication Union, _c2018.
300			_a7 p.
336			_atexto (visual) _2isbdcontent
337			_aelectrónico _2isbdmedia
338			_arecurso en línea _2rdacarrier
520	3		_aReceptors on-board satellites are being implemented to track civil aircrafts all around the world. This new scenario requires novel methods to process the signals in order to efficiently retrieve more updated and reliable position and status data of every aircraft. To reach the required performance, it is indeed needed to engage carefully chosen algorithms of data analysis and processing. Machine learning algorithms, in particular k-nearest neighbors and support vector machines, are employed to estimate the potential success in decodifying ADS-B messages in highly congested areas, and simulations are performed to obtain the training and testing signals. First, the ADS-B communication system is described; second, multivariate analysis and machine learning algorithms are studied. Finally, the results obtained from machine learning methods are compared and future studies are proposed.
650		0	_aTecnologías habilitadoras digitales _918
653		4	_aScience and Technology
700	1		_aRiolfo, Martín Gabriel _93358
773	0		_04843 _tITU Journal: ICT Discoveries _gVol. 2018, no. 2, p. 103-109 _q2018:2<103 _x2616-8375
856	4	0	_aitu-ilibrary.org _uhttps://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I2-2018-13-PDF-E.pdf _yAcceso al documento _x0 _qpdf
942			_cART _2udc