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005			20260817161123.0
008			220914s2022 th uu m rtt 0| a1eng d
035			_a.b1239175x
099		9	_aAIT Thesis no.TC-22-01
100	1		_aBajracharya, Oja
245	1	0	_a2D DOA estimation for volumetric antenna array using convolutional neural network for smart antenna application
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2022
300			_a34 leaves : _bill.
490	1		_aThesis ; _vno. TC-22-01
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Telecommunications
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2022
520			_aDirection of Arrival (DOA) Estimation is amongst the pivotal technologies in the field of smart antenna systems. Smart antenna systems are capable of locating and tracking signals by the use of various DOA algorithms. Hence, these systems can dynamically adapt to enhance the reception of the signals in the necessary directions and minimize the effect of the interfering signals. Two-dimensional (2D) DOA estimation provides ample spatial statistics of the signals impinging in the antenna array, and has more practical significance in smart antenna applications for source localization. Since regular planar antenna arrays are unable to meet the requirements for the quick and precise localization of mobile sources, the concept of volumetric antenna arrays has been introduced. However, there have been limited research done on DOA estimation of these volumetric antenna arrays. A novel approach to 2D DOA estimation of a volumetric antenna array using Convolutional Neural Network (CNN) is proposed in this study. The 2D DOA prediction network takes the covariance matrices of the various angle pairs as its input. These matrices are computed from the volumetric extension of the planar array which is preprocessed for better feature extraction. In addition, the signals simulated undergo multipath fading during its generation, to closely resemble a real-world signal. Finally, the CNN outputs the predicted angle pairs for a given number of test samples of angle pair covariance matrices.
650		0	_aNeural networks (Computer science)
650		0	_aAntenna arrays
700	0		_aAttaphongse Taparugssanagorn, _eChairperson
700	0		_aTeerapat Sanguankotchakorn, _eExamination Committee
700	0		_aMongkol Ekpanyapong, _eExamination Committee
710	2		_aAsian Institute of Technology Scholarships, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. TC-22-01
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B17272
907			_a.b1239175x _bmnait _cm
902			_a240419
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909			_aBarcode : 30020220005659 _bCREATED : 2022-09-13 _cRECORD # : i1339910x _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c20 _d20