Proximity operations of CubeSats via sensor fusion of ultra-wideband range measurements with rate gyroscopes, accelerometers and monocular vision

TL;DR

This paper presents a robust sensor fusion algorithm using an extended Kalman filter that combines ultra-wideband radar, IMU, and monocular vision for precise CubeSat proximity operations and docking.

Contribution

It introduces a novel sensor fusion approach with outlier rejection and measurement weighting to improve pose estimation robustness in satellite proximity tasks.

Findings

Validated with low-cost sensors in experimental setups.

Achieved accurate pose estimation for satellite docking.

Demonstrated robustness in the presence of measurement outliers.

Abstract

A robust pose estimation algorithm based on an extended Kalman filter using measurements from accelerometers, rate gyroscopes, monocular vision and ultra-wideband radar is presented. The sensor fusion and pose estimation algorithm incorporates Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust filter performance in the case of sudden range measurements led by the absence of measurements due to range limitations of radar transceivers. The estimator is further validated through an experimental analysis using low-cost radar, IMU and camera sensors. The pose estimate is utilized to perform proximity operations and docking of Transforming Proximity Operations and Docking Service (TPODS) satellite modules with a fixed target.