Title :
Multi-process constrained estimation
Author :
Hintz, K.J. ; Mcvey, E.S.
Author_Institution :
US Naval Surface Weapons Center, Dahlgren, VA, USA
Abstract :
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
Keywords :
Kalman filters; channel capacity; entropy; state estimation; Kalman filter state estimator; constrained communications channel; estimation entropy; information flow; multiple nonstationary processes; sampling sequence; stationary processes; Aerospace testing; Decision making; Electrons; Fluid flow measurement; Information processing; Notice of Violation; Radar detection; Sensor systems; State estimation; System testing;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on
