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

Rapport Rapport technique Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration eng 2023-09-21 author Bakalli Gaetan author Cucci Davide Antonio 935364 author Radi Ahmed author El-Sheimy Naser author Molinari Roberto author Scaillet Olivier 0000-0002-3948-6891 134311 Recherche et enseignement GSEM (RESEM) author Guerrier Stéphane 625357 Recherche et enseignement GSEM (RESEM) 14 <p>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 cor- rectly 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.</p> <p>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</p> Generalized Method of Wavelet Moments In- ertial Sensor Calibration Stochastic Error Extended Kalman Filter Navigation Dewey 650 116 Geneva Finance Research Institute S4 false 852 Probabilités et statistique af162e47-2194-4f03-bd7f-23e2cb8c7ac3 Report Public MS_GMWM.pdf 1540798 application/pdf 2889e72eadad1e899aa5290985d9b2eede5c027c068dac37a4994e1601127155 openAccess Faculté d'économie et de management / Geneva Finance Research Institute

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Cucci, Davide Antonio