Academic Journal
A Stochastic Approach to Noise Modeling for Barometric Altimeters.
| Title: | A Stochastic Approach to Noise Modeling for Barometric Altimeters. |
|---|---|
| Authors: | Sabatini, Angelo Maria, Genovese, Vincenzo |
| Source: | Sensors (14248220); Nov2013, Vol. 13 Issue 11, p15692-15707, 16p |
| Subject Terms: | Altimeters, Altitude measurements, Stochastic models, Mathematical models, Human locomotion |
| Abstract: | The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions. [ABSTRACT FROM AUTHOR] |
| Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=14248220&ISBN=&volume=13&issue=11&date=20131101&spage=15692&pages=15692-15707&title=Sensors (14248220)&atitle=A%20Stochastic%20Approach%20to%20Noise%20Modeling%20for%20Barometric%20Altimeters.&aulast=Sabatini%2C%20Angelo%20Maria&id=DOI:10.3390/s131115692 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Stochastic Approach to Noise Modeling for Barometric Altimeters. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sabatini%2C+Angelo+Maria%22">Sabatini, Angelo Maria</searchLink><br /><searchLink fieldCode="AR" term="%22Genovese%2C+Vincenzo%22">Genovese, Vincenzo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sensors (14248220); Nov2013, Vol. 13 Issue 11, p15692-15707, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Altimeters%22">Altimeters</searchLink><br /><searchLink fieldCode="DE" term="%22Altitude+measurements%22">Altitude measurements</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Human+locomotion%22">Human locomotion</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s131115692 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 15692 Subjects: – SubjectFull: Altimeters Type: general – SubjectFull: Altitude measurements Type: general – SubjectFull: Stochastic models Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Human locomotion Type: general Titles: – TitleFull: A Stochastic Approach to Noise Modeling for Barometric Altimeters. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sabatini, Angelo Maria – PersonEntity: Name: NameFull: Genovese, Vincenzo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 13 – Type: issue Value: 11 Titles: – TitleFull: Sensors (14248220) Type: main |
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