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

TL;DR

This paper investigates multi-signal methods for combining multiple error signal replicates in inertial sensor calibration, aiming to enhance navigation accuracy by accounting for parameter variations across replicates.

Contribution

It introduces and evaluates approaches that leverage all available replicates, considering parameter variability, to improve error modeling and navigation performance.

Findings

Improved navigation accuracy through multi-signal approaches.

Reliable estimation of uncertainty in navigation solutions.

Validation of methods on real inertial sensor data.

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.