Precomputing Process Noise Covariance for Onboard Sequential Filters

Process 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 c...

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Bibliographic Details
Published in:Journal of guidance, control, and dynamics control, and dynamics, 2017-08, Vol.40 (8), p.2062-2075
Main Authors: Olson, Corwin G., Russell, Ryan P., Carpenter, J. Russell
Format: Article
Language:English
Subjects:
Acceleration
Aerospace engineering
Analysis of covariance
Apollo asteroids
Covariance
Dualism
Engineering (General)
Error analysis
Laboratories
Navigation
Noise
Noise propagation
Nonlinear dynamics
Onboard
Parameter uncertainty
Permissible error
Process parameters
Representations
Spacecraft
State estimation
Systems Analysis And Operations Research
Uncertainty
Uncertainty analysis
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Description
Summary:Process 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.
ISSN:0731-5090
1533-3884
DOI:10.2514/1.G002144