Multi-process constrained estimation

A method that maximizes the information flow through a constrained communications channel when it is desired to estimate the state of multiple nonstationary processes is described. The concept of a constrained channel is introduced as a channel that is not capable of transferring all of the informat...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1991-01, Vol.21 (1), p.237-244
Main Authors: Hintz, K.J., McVey, E.S.
Format: Article
Language:English
Subjects:
Aerospace testing
Applied sciences
Decision making
Electrons
Exact sciences and technology
Fluid flow measurement
Information processing
Information, signal and communications theory
Radar detection
Sensor systems
State estimation
System testing
Telecommunications and information theory
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Summary:A method that maximizes the information flow through a constrained communications channel when it is desired to estimate the state of multiple nonstationary processes is described. The concept of a constrained channel is introduced as a channel that is not capable of transferring all of the information required. A measure of information is developed based on the estimation entropy utilizing the Kalman filter state estimator. It is shown that this measure of information can be used to determine which process to observe in order to maximize a measure of global information flow. For stationary processes, the sampling sequence can be computed a priori, but nonstationary processes require real-time sequence computation.< >
ISSN:0018-9472
2168-2909
DOI:10.1109/21.101154