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Theoretical limitations of allan variance-based regression for time series model estimation

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Published in IEEE Signal Processing Letters. 2016, vol. 23, no. 5, p. 597-601
Abstract This paper formally proves the statistical inconsistency of the Allan variance-based estimation of latent (composite) model parameters. This issue has not been sufficiently investigated and highlighted since it is a technique that is still being widely used in practice, especially within the engineering domain. Indeed, among others, this method is frequently used for inertial sensor calibration which often deals with latent time series models and practitioners in these domains are often unaware of its limitations. To prove the inconsistency of this method we firstly provide a formal definition and subsequently deliver its theoretical properties, highlighting its limitations by comparing it with another statistically sound method.
Keywords Latent time serie modelState Space ModelSensor CalibrationInertial Measurement UnitError Modelling
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GUERRIER, Stéphane, MOLINARI, Roberto Carlo, STEBLER, Yannick Sébastien. Theoretical limitations of allan variance-based regression for time series model estimation. In: IEEE Signal Processing Letters, 2016, vol. 23, n° 5, p. 597-601. https://archive-ouverte.unige.ch/unige:96023

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Deposited on : 2017-08-07

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