Applying Gaussian distributed constraints to Gaussian distributed variables
Abstract
This paper develops an analytical method of truncating inequality constrained Gaussian distributed variables where the constraints are themselves described by Gaussian distributions. Existing truncation methods either assume hard constraints, or use numerical methods to handle uncertain constraints. The proposed approach introduces moment-based Gaussian approximations of the truncated distribution. This method can be applied to numerous problems, with the motivating problem being Kalman filtering with uncertain constraints. In a simulation example, the developed method is shown to outperform unconstrained Kalman filtering by over 40% and hard-constrained Kalman filtering by over 17%.
Cite
@article{arxiv.1606.02205,
title = {Applying Gaussian distributed constraints to Gaussian distributed variables},
author = {Andrew W. Palmer and Andrew J. Hill and Steven J. Scheding},
journal= {arXiv preprint arXiv:1606.02205},
year = {2016}
}
Comments
34 pages, 12 figures, accepted to Information Fusion. Updated to fix minor errors in equations (33), (34), and (B.12)