Advances in Observer Techniques for Ballistic Missile Defense Filtering Algorithms

This report investigates the idea of utilizing Luenberger's minimal-order observer as an alternate to the Kalman filter for obtaining state estimates in linear discrete-time stochastic systems. More specifically, this dissertation presents a solution to the problem of constructing an optimal mi...

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Bibliographic Details
Main Author: Novak,Leslie M
Format: Report
Language:English
Subjects:
ALGORITHMS
Antimissile Defense Systems
COMPUTERIZED SIMULATION
DISCRETE DISTRIBUTION
GAUSSIAN NOISE
INVARIANCE
KALMAN FILTERING
LINEAR SYSTEMS
MARKOV PROCESSES
MATHEMATICAL FILTERS
OBSERVERS
RADAR TRACKING
Statistics and Probability
STOCHASTIC PROCESSES
TIME INTERVALS
TRANSFORMATIONS(MATHEMATICS)
WHITE NOISE
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Summary:This report investigates the idea of utilizing Luenberger's minimal-order observer as an alternate to the Kalman filter for obtaining state estimates in linear discrete-time stochastic systems. More specifically, this dissertation presents a solution to the problem of constructing an optimal minimal-order observer for linear discrete-time stochastic systems where optimality is in the mean-square sense. The approach taken in this dissertation leads to a completely unified theory for the design of optimal minimal-order observers and is applicable to both time-varying and time-invariant linear discrete systems. The basic solution to the problem is first obtained for that class of systems having Gaussian white noise disturbances. The solution is based on a special linear transformation which transforms the given time-varying discrete-time state equations into an equivalent state space which is extremely convenient from the standpoint of observer design. (Author)