Landmark-Selection Optimization Method for Autonomous Optical Planetary Landing Navigation Systems Using a Relaxation Optimization Algorithm

2022 
A landmark-selection optimization method based on a relaxation optimization algorithm is proposed for autonomous optical planetary landing navigation systems. The objective function of the optimal problem is constructed using the Cramer–Rao lower bound and the Mahalanobis distance; the Cramer–Rao lower bound is used to evaluate the accuracy of system-state estimation, and the Mahalanobis distance is used to normalize the state variables so that discrepancies caused by the different dimensions of different state variables can be eliminated. In this way, the optimization results can notably improve the accuracy of navigation. To satisfy the constraint of limited on-board computational resources, a relaxation optimization algorithm is used to find the optimal solutions. Simulations were used to compare the navigation performance of different landmark-selection methods, and the results indicate that the proposed method has notable advantages.
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