Scale-wise Variance Minimization for Optimal Virtual Signals: An Approach for Redundant Gyroscopes

TL;DR

This paper introduces a non-parametric wavelet-based method to optimally combine measurements from multiple gyroscopes, reducing noise without assuming specific error models, applicable to various inertial sensors and measurement domains.

Contribution

The work presents a novel wavelet cross-covariance approach for sensor fusion that is model-free and broadly applicable, advancing the state of the art in sensor array signal processing.

Findings

The method effectively reduces measurement noise in simulated and real gyroscope data.

The approach is theoretically sound and supported by simulations and practical applications.

It can be extended to other inertial sensors and measurement systems.

Abstract

The increased use of low-cost gyroscopes within inertial sensors for navigation purposes, among others, has brought to the development of a considerable amount of research in improving their measurement precision. Aside from developing methods that allow to model and account for the deterministic and stochastic components that contribute to the measurement errors of these devices, an approach that has been put forward in recent years is to make use of arrays of such sensors in order to combine their measurements thereby reducing the impact of individual sensor noise. Nevertheless combining these measurements is not straightforward given the complex stochastic nature of these errors and, although some solutions have been suggested, these are limited to certain specific settings which do not allow to achieve solutions in more general and common circumstances. Hence, in this work we put forward a non-parametric method that makes use of the wavelet cross-covariance at different scales to combine the measurements coming from an array of gyroscopes in order to deliver an optimal measurement signal without needing any assumption on the processes underlying the individual error signals. We also study an appropriate non-parametric approach for the estimation of the asymptotic covariance matrix of the wavelet cross-covariance estimator which has important applications beyond the scope of this work. The theoretical properties of the proposed approach are studied and are supported by simulations and real applications, indicating that this method represents an appropriate and general tool for the construction of optimal virtual signals that are particularly relevant for arrays of gyroscopes. Moreover, our results can support the creation of optimal signals for other types of inertial sensors other than gyroscopes as well as for redundant measurements in other domains other than navigation.