Position and Velocity estimation of Re-entry Vehicles using Fast Unscented Kalman Filters

TL;DR

This paper introduces two fast UKF-based methods, SPUKF and ESPUKF, for re-entry vehicle position and velocity estimation, achieving a balance between accuracy and processing time compared to traditional filters.

Contribution

The paper proposes two novel UKF variants, SPUKF and ESPUKF, that reduce processing time while maintaining high estimation accuracy in re-entry vehicle navigation.

Findings

SPUKF has better accuracy than EKF with significantly reduced processing time.

ESPUKF achieves accuracy comparable to UKF with much faster processing.

UKF outperforms EKF in accuracy but requires more computation.

Abstract

Application of two new UKF based estimation techniques with reduced processing time in re-entry vehicle position and velocity estimation problem using ground-based range and elevation measurements is presented. The first method is called the Single Propagation Unscented Kalman Filter (SPUKF) where, the a postiriori state is propagated only once and then the sampled sigma points at the next time state are approximated by the first-order Taylor Series terms. In the second method called the Extrapolated Single Propagation Unscented Kalman Filter (ESPUKF), the sigma points are approximated to the second-order Taylor Series terms using the Richardson Extrapolation. The EKF, SPUKF, ESPUKF and the UKF are utilized in a re-entry vehicle navigation scenario using range and elevation measurements. The estimation accuracies and the processing times for different algorithms are compared for the scenario. The result demonstrates that the UKF provides better accuracy than the EKF but requires more processing time. The SPUKF accuracy is better than the EKF and the processing time is significantly less than the UKF. However, the accuracy of the SPUKF is less than the UKF. The ESPUKF provides estimation accuracy comparable to the UKF and the processing time is also significantly reduced.