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Precomputing Process Noise Covariance for Onboard Sequential FiltersProcess noise is often used in estimation filters to account for unmodeled and mismodeled accelerations in the dynamics. The process noise covariance acts to inflate the state covariance over propagation intervals, increasing the uncertainty in the state. In scenarios where the acceleration errors change significantly over time, the standard process noise covariance approach can fail to provide effective representation of the state and its uncertainty. Consider covariance analysis techniques provide a method to precompute a process noise covariance profile along a reference trajectory using known model parameter uncertainties. The process noise covariance profile allows significantly improved state estimation and uncertainty representation over the traditional formulation. As a result, estimation performance on par with the consider filter is achieved for trajectories near the reference trajectory without the additional computational cost of the consider filter. The new formulation also has the potential to significantly reduce the trial-and-error tuning currently required of navigation analysts. A linear estimation problem as described in several previous consider covariance analysis studies is used to demonstrate the effectiveness of the precomputed process noise covariance, as well as a nonlinear descent scenario at the asteroid Bennu with optical navigation.
Document ID
Document Type
Reprint (Version printed in journal)
External Source(s)
Olson, Corwin G. (Texas Univ. Austin, TX, United States)
Russell, Ryan P. (Texas Univ. Austin, TX, United States)
Carpenter, J. Russell (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
March 26, 2018
Publication Date
April 24, 2017
Publication Information
Publication: Journal of Guidance Control and Dynamics
Volume: 40
Issue: 8
ISSN: 0731-5090
Subject Category
Engineering (General)
Systems Analysis and Operations Research
Report/Patent Number
Funding Number(s)
Distribution Limits
sequential filters
noise covariance

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