Beamforming pointing error of a triaxial velocity sensor under gain uncertainties

A "triaxial velocity sensor" consists of three uniaxial velocity sensors, which are nominally identical, orthogonally oriented among themselves, and co-centered at one point in space. A triaxial velocity sensor measures the acoustic particle velocity vector, by its three Cartesian componen...

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Main Authors: Lin, Tsair Chuan, Wong, Kainam Thomas, Cordel, Macario O., Ilao, Joel P.
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Published: Animo Repository 2016
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3818
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4820/type/native/viewcontent/1.4962290
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-48202022-07-20T03:02:43Z Beamforming pointing error of a triaxial velocity sensor under gain uncertainties Lin, Tsair Chuan Wong, Kainam Thomas Cordel, Macario O. Ilao, Joel P. A "triaxial velocity sensor" consists of three uniaxial velocity sensors, which are nominally identical, orthogonally oriented among themselves, and co-centered at one point in space. A triaxial velocity sensor measures the acoustic particle velocity vector, by its three Cartesian components, individually component-by-component, thereby offering azimuth-elevation two-dimensional spatial directivity, despite the physical compactness that comes with the collocation of its three components. This sensing system's azimuth-elevation beam-pattern has been much analyzed in the open literature, but only for an idealized case of the three uniaxial velocity sensors being exactly identical in gain. If this nominal identity is violated among the three uniaxial velocity sensors, as may occur in practical hardware, what would happen to the corresponding "spatial matched filter" beam-pattern's peak direction? How would this effective peak direction deviate from the nominal "look direction"? This paper, by modeling each uniaxial velocity sensor's gain as stochastic, derives this deviation's statistical mean and variance, analytically in closed mathematical forms. This analytical derivation is verified by Monte Carlo simulations. © 2016 Acoustical Society of America. 2016-09-01T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/3818 info:doi/10.1121/1.4962290 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4820/type/native/viewcontent/1.4962290 Faculty Research Work Animo Repository Motion detectors Speed Monte Carlo method Stochastic systems Artificial Intelligence and Robotics
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Motion detectors
Speed
Monte Carlo method
Stochastic systems
Artificial Intelligence and Robotics
spellingShingle Motion detectors
Speed
Monte Carlo method
Stochastic systems
Artificial Intelligence and Robotics
Lin, Tsair Chuan
Wong, Kainam Thomas
Cordel, Macario O.
Ilao, Joel P.
Beamforming pointing error of a triaxial velocity sensor under gain uncertainties
description A "triaxial velocity sensor" consists of three uniaxial velocity sensors, which are nominally identical, orthogonally oriented among themselves, and co-centered at one point in space. A triaxial velocity sensor measures the acoustic particle velocity vector, by its three Cartesian components, individually component-by-component, thereby offering azimuth-elevation two-dimensional spatial directivity, despite the physical compactness that comes with the collocation of its three components. This sensing system's azimuth-elevation beam-pattern has been much analyzed in the open literature, but only for an idealized case of the three uniaxial velocity sensors being exactly identical in gain. If this nominal identity is violated among the three uniaxial velocity sensors, as may occur in practical hardware, what would happen to the corresponding "spatial matched filter" beam-pattern's peak direction? How would this effective peak direction deviate from the nominal "look direction"? This paper, by modeling each uniaxial velocity sensor's gain as stochastic, derives this deviation's statistical mean and variance, analytically in closed mathematical forms. This analytical derivation is verified by Monte Carlo simulations. © 2016 Acoustical Society of America.
format text
author Lin, Tsair Chuan
Wong, Kainam Thomas
Cordel, Macario O.
Ilao, Joel P.
author_facet Lin, Tsair Chuan
Wong, Kainam Thomas
Cordel, Macario O.
Ilao, Joel P.
author_sort Lin, Tsair Chuan
title Beamforming pointing error of a triaxial velocity sensor under gain uncertainties
title_short Beamforming pointing error of a triaxial velocity sensor under gain uncertainties
title_full Beamforming pointing error of a triaxial velocity sensor under gain uncertainties
title_fullStr Beamforming pointing error of a triaxial velocity sensor under gain uncertainties
title_full_unstemmed Beamforming pointing error of a triaxial velocity sensor under gain uncertainties
title_sort beamforming pointing error of a triaxial velocity sensor under gain uncertainties
publisher Animo Repository
publishDate 2016
url https://animorepository.dlsu.edu.ph/faculty_research/3818
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4820/type/native/viewcontent/1.4962290
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