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A stochastic approach to noise modeling for barometric altimeters
Angelo Maria Sabatini1, Vincenzo Genovese
1The BioRobotics Institute, Scuola Superiore Sant'Anna/P.zza Martiri della Libertà, 33, 56124 Pisa, Italy. sabatini@sssup.it.
Sensors (Basel, Switzerland)
|November 21, 2013
Summary
This study models barometric altimeter noise to improve human motion tracking. The developed stochastic model enhances short-time height change detection, addressing limitations in current barometric altimetry for motion analysis.
Area of Science:
- Sensor technology
- Stochastic modeling
- Human motion analysis
Background:
- Barometric altimeters have limitations for accurate human motion tracking due to poor resolution and drifting.
- Existing methods struggle with short-time height change detection.
Purpose of the Study:
- To develop a stochastic model for barometric altimeter noise.
- To improve the accuracy of short-time human motion tracking using barometric data.
Main Methods:
- Decomposition of barometric altimeter noise into deterministic, Gauss-Markov (GM), and uncorrelated components.
- Application of Autoregressive-Moving Average (ARMA) system identification.
- Testing of moving average and whitening filters in dynamic motion experiments.
Main Results:
- The stochastic model captures key statistical properties of barometric altimeter noise.
- ARMA techniques effectively identified the GM component's correlation structure and noise standard deviations.
- Moving average filters showed capability in short-time tracking of small-amplitude, low-frequency motions.
Conclusions:
- The developed stochastic model offers a pathway to more accurate barometric altimetry for human motion tracking.
- The noise decomposition and modeling address critical performance limitations.
- Further research can refine filter combinations for enhanced motion tracking applications.
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