Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 41, Issue 2, Pages 159-171Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207720903042970
Keywords
Kalman filter; state constraints; estimation; probability density function; gas turbine engines; health monitoring; optimal filtering; constrained filtering
Categories
Funding
- NASA
Ask authors/readers for more resources
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).
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available