Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Peter Maybeck, PhD


The Air Force Institute of Technology has been involved in developing Kalman filter based trackers of ballistic missiles for 15 years. The goal of this thesis is to develop a Multiple Model Adaptive Estimator MMAE that tracks the missile plume using a forward looking infrared sensor and the missile hardbody center-of-mass additionally using low energy laser returns for the purpose of directing a high power laser to incapacitate the missile. The missile plume pogos about an offset equilibrium point relative to the hardbody center-of-mass with an amplitude and frequency of oscillation that are not precisely known a priori. The MMAE algorithm estimates these parameters to improve performance in tracking the hardbody center-of-mass. To accomplish this MMAE structure, single Kalman filters were developed and tested at the different parameter values. A Kalman filter residual analysis was used on these working single filters to define the MMAE structure that provided the most effective adaptation and most accurate target tracking. A three-filter MMAE structure gave the lowest hardbody center-of-mass tracking errors. The two-dimensional parameter space, pogo amplitude and frequency, was successfully partitioned according to the frequency of oscillation. When the plume pogo amplitude is large, the MMAE structure substantially reduces the tracking errors of the hardbody center-of-mass, compared to a tracker without adaptive pogo estimation.

AFIT Designator


DTIC Accession Number



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