Interaction of Pose Estimation and Online Dynamic Modeling for a Small Inspector Spacecraft

Abstract

Close-proximity operations between spacecraft allow for docking and inspection of a target vehicle. Very small spacecraft are well-suited for proximity activities and are of growing interest in the aerospace community. However, due to size and power constraints, small vehicles cannot carry traditional precision navigation systems and generally have noisy sensor and actuator options. This paper presents two techniques for improved autonomous, on-board navigation that account for these poorly observable states. First, an Unscented Kalman Filter is implemented for pose estimation which incorporates orbital dynamics and quaternion rotation. Second, online Bayes Linear Regression (BLR) models the time-varying thruster dynamics. The BLR is then used to complement an unreliable sensor in the UKF, improving pose prediction. These techniques have been demonstrated successfully on a simulated small inspector vehicle and are being integrated in the Bandit inspector spacecraft.