ADAPTIVE MEASUREMENT ERROR COVARIANCE ADJUSTMENT FOR VISION-AIDED SPACECRAFT NAVIGATION

Abdurrahim Muratoglu, Halil Ersin Soken and Ozan Tekinalp

This paper introduces an adaptive method for spacecraft navigation that dynamically adjusts Kalman Filter parameters using Cramer-Rao Lower Bound analysis. While line-of-sight based visual observations of celestial bodies yield valuable positional references that enhance filter-based solutions for interplanetary navigation, their accuracy varies with the ranges to observed bodies and the angular separation between these observations. Our approach quantifies theoretical lower error variance limits for each observation scenario throughout a trajectory, enabling more precise and reliable specification of measurement covariance matrices. Simulation results demonstrate improved position estimation accuracy throughout translunar trajectories, particularly during geometrically challenging segments where conventional range based methods struggle to maintain consistent estimation performance.

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