Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration

Gaetan Bakalli, Davide Cucci, Ahmed Radi, Naser El-Sheimy, Roberto Molinari, Olivier Scaillet and Stéphane Guerrier
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Gaetan Bakalli: University of Geneva - Geneva School of Economics and Management
Davide Cucci: University of Geneva
Ahmed Radi: University of Calgary
Naser El-Sheimy: University of Calgary
Roberto Molinari: Auburn University
Stéphane Guerrier: University of Geneva - Geneva School of Economics and Management

No 21-70, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. In this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.

Keywords: Generalized Method of Wavelet Moments; Inertial Sensor Calibration; Stochastic Error; Extended Kalman Filter; Navigation (search for similar items in EconPapers)
Pages: 16 pages
Date: 2021-10
New Economics Papers: this item is included in nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2170

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