A state-space approach to optimal level-crossing prediction for linear Gaussian processes

Authors:
Rodney A. Martin
Affiliations:
Intelligent Systems Division and the Intelligent Data Understanding Group, NASA Ames Research Center, Moffett Field, CA
Venue:
IEEE Transactions on Information Theory
Year:
2010

Citing 3
Cited 1

Detection of abrupt changes: theory and application

Detection of abrupt changes: theory and application
Applied Optimal Control and Estimation

Applied Optimal Control and Estimation
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Pattern Recognition

Optimal level-crossing prediction for jump linear MIMO dynamical systems

Automatica (Journal of IFAC)

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Abstract

In this paper, approximations of an optimal level-crossing predictor for a zero-mean stationary linear dynamical system driven by Gaussian noise in state-space form are investigated. The study of this problem is motivated by the practical implications for design of an optimal alarm system, which will elicit the fewest false alarms for a fixed detection probability in this context. This work introduces the use of Kalman filtering in tandem with the optimal level-crossing prediction problem. It is shown that there is a negligible loss in overall accuracy when using approximations to the theoretically optimal predictor, at the advantage of greatly reduced computational complexity.