Document Type
Article
Publication Date
2-1-2010
Publication Title
International Journal of Systems Science
Abstract
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This article develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the probability density function (PDF) of the Kalman filter estimate at the known constraints and then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variable constraints increases the computational effort of the filter but also improves its estimation accuracy. The improvement is demonstrated via simulation results obtained from a turbofan engine model. It is also shown that the truncated Kalman filter may provide a more accurate way of incorporating inequality constraints than other constrained filters (e.g. the projection approach to constrained filtering).
Repository Citation
Simon, Daniel J. and Simon, Donald L., "Constrained Kalman Filtering via Density Function Truncation for Turbofan Engine Health Estimation" (2010). Electrical and Computer Engineering Faculty Publications. 17.
https://engagedscholarship.csuohio.edu/enece_facpub/17
Original Citation
Dan, S., & Simon, D. L. (2010). Constrained Kalman filtering via density function truncation for turbofan engine health estimation. International Journal Of Systems Science, 41(2), 159-171. doi:10.1080/00207720903042970
DOI
10.1080/00207720903042970
Version
Postprint
Publisher's Statement
(c) 2010 Taylor & Francis
Volume
41
Issue
2
