Attitude Estimation Using Scalar Measurements

TL;DR

This paper introduces a novel scalar measurement-based attitude estimation framework for rigid bodies, utilizing a linear Kalman filter within an LTV model to achieve robust and accurate orientation estimation even with partial sensor data.

Contribution

It presents a new scalar measurement approach and reformulates attitude estimation within an LTV framework, guaranteeing stability and robustness with partial data.

Findings

Effective attitude estimation with partial measurements

Guarantees of global stability under observability conditions

Robust performance demonstrated in simulations

Abstract

This paper revisits the problem of orientation estimation for rigid bodies through a novel framework based on scalar measurements. Unlike traditional vector-based methods, the proposed approach enables selective utilization of only the reliable axes of vector measurements while seamlessly incorporating alternative scalar modalities such as Pitot tubes, barometers with range sensors, and landmark-based constraints. The estimation problem is reformulated within a linear time-varying (LTV) framework, allowing the application of a deterministic linear Kalman filter. This design guarantees Global Uniform Exponential Stability (GES) under the Uniform Observability (UO) condition. Simulation results demonstrate the effectiveness of the proposed approach in achieving robust and accurate attitude estimation, even with partial vector measurements that simulate sensor axis failure.