Covariance Analysis of Attitude and Angular Rate Estimation using Accelerometers

TL;DR

This paper presents a linear least squares-based method for estimating angular velocity and acceleration using accelerometers, optimizing sensor configuration and providing practical sign determination techniques.

Contribution

It introduces a novel framework leveraging linear least squares for quadratic functions of angular velocity, with insights into sensor array configuration and sign determination methods.

Findings

Effective covariance approximation compared to Monte Carlo methods

Optimized accelerometer array configurations for improved estimator performance

Practical methods for sign determination of the spin axis

Abstract

In this work a method for using accelerometers for the determination of angular velocity and acceleration is presented. Minimum sensor requirements and insights into how an array of accelerometers can be configured to maximize estimator performance are considered. The framework presented utilizes linear least squares to estimate functions that are quadratic in angular velocity. Simple methods for determining the sign of the spin axis and the linearized covariance approximation are presented and found to perform quite effectively when compared to results obtained by Monte Carlo.