Abstract
This research presents an innovative approach designed to enhance the performance of crater-based navigation systems. The core of this approach revolves around proposing a novel method for calculating and incorporating the degree of perturbation observed in matched craters. The foundation of our algorithm lies in the concept of comparing the similarity of projective invariants between image and database craters during the crater matching process. The degree of perturbation in each extracted crater is quantified and normalized using a multivariate Gaussian model. These values are subsequently employed as observation weights within the navigation system. Simulation results confirm the effectiveness of our approach in accurately computing weights that reflect the varying levels of error between craters. Moreover, when integrated into the navigation system, proposed method substantially elevates navigation performance.
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