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Sensors 2013, 13(11), 15692-15707; doi:10.3390/s131115692

A Stochastic Approach to Noise Modeling for Barometric Altimeters

The BioRobotics Institute, Scuola Superiore Sant'Anna/P.zza Martiri della Libertà, 33, 56124 Pisa, Italy
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Received: 30 August 2013 / Revised: 22 October 2013 / Accepted: 13 November 2013 / Published: 18 November 2013
(This article belongs to the Section Physical Sensors)
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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.
Keywords: barometric altimeters; stochastic modeling; system identification; motion tracking barometric altimeters; stochastic modeling; system identification; motion tracking
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Sabatini, A.M.; Genovese, V. A Stochastic Approach to Noise Modeling for Barometric Altimeters. Sensors 2013, 13, 15692-15707.

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