Computation of Extended Robust Kalman Filter for Real-Time Attitude and Position Estimation

TL;DR

This paper presents a real-time implementation of the extended robust Kalman filter using QR decomposition with Givens rotation, applied to attitude and position estimation for cargo vehicles, demonstrating its effectiveness and computational efficiency.

Contribution

It introduces a method for real-time computation of the ERKF on parallel devices using Givens rotation, enhancing practical applicability in vehicle navigation.

Findings

Successful real-time ERKF implementation on parallel hardware.

Effective attitude and position estimation for cargo transport.

Validation of Givens rotation method for filter performance.

Abstract

This paper deals with the implementation of the extended robust Kalman filter (ERKF) which was developed considering uncertainties in the parameter matrices of the underlying state-space model. A key contribution of this work is the demonstration of a method for real-time computation of the filter on parallel computing devices. The solution of the filter is expressed as a set of simultaneous linear equations, which can then be evaluated based on QR decomposition using Givens rotation. This paper also presents the application of the ERKF in the development of an attitude and position reference system for a cargo transport vehicle. This work concludes by analyzing the performance of the ERKF and verifying the validity of the Givens rotation method.