Graduate Thesis Or Dissertation


Supporting Autonomous Navigation with Flash Lidar Images in Proximity to Small Celestial Bodies Public Deposited

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  • Spacecraft navigation in proximity to small celestial bodies, such as asteroids and comets, is challenging due to their complex dynamical environment and the lag in communications from Earth to the spacecraft. Increasing autonomous spacecraft navigation reduces the burden of ground-based planning and modeling, and enables insightful mission profiles. The state-of-the-art of relative spacecraft navigation uses optical images, requires an iterative procedure, and currently must be performed on the ground. A flash lidar instrument instantaneously returns a 3D elevation map of its target and shows promise for advancing autonomous spacecraft navigation. Using this instrument as a relative measurement source for navigation performed similarly or better than landmark-based navigation from optical images. The model-based approach used to compute the onboard flash lidar images eliminated the correlation procedures required of landmark-based approaches, and reduced the computational load. An extended Kalman filter (EKF), an unscented Kalman filter (UKF), and an iterative least-squares (LS) filter were investigated in this analysis. The iterative LS filter iterated the estimation state at each observation time, produced smaller errors than the EKF and UKF, and did not encounter filter saturation. The image properties of the flash lidar measurements allowed for pointing to be estimated. The UKF and LS filters were robust to initial position errors as long as an overlap occurred between the observed and computed flash lidar images. When introducing shape modeling errors, the filters did not diverge, and the majority of the state errors were captured with a sequential consider covariance analysis. Using the image properties of the flash lidar images, and assuming inertial spacecraft pointing knowledge, the filter was initialized through pre-processing algorithms and the iterative LS algorithm. Optimally reducing the number of altimetry measurements processed by maximizing their information contribution increased the computational efficiency and combated filter saturation without sacrificing accuracy.
Date Issued
  • 2017
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  • 2019-11-14
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