Title page for ETD etd-07282008-135232


Type of Document Dissertation
Author Caputi, Mauro J.
URN etd-07282008-135232
Title NonGaussian estimation using a modified Gaussian sum adaptive filter
Degree PhD
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Moose, Richard L. Committee Chair
Baumann, William T. Committee Member
Kohler, Werner E. Committee Member
Nunnally, Charles E. Committee Member
VanLandingham, Hugh F. Committee Member
Keywords
  • Kalman filtering
Date of Defense 1991-04-14
Availability unrestricted
Abstract
This investigation is concerned with effective state estimation of a system

driven by an unknown nonGaussian input with additive white Gaussian noise, and

observed by measurements containing feedthrough of the same nonGaussian input

and corrupted by additional white Gaussian noise. A Gaussian sum (GS) approach

has previously been developed [6-8] which can cope with the nonGaussian nature of

the input signal. Due to a serious growing memory problem in this approach, a

modified Gaussian sum (MGS) estimation technique is developed that avoids the

growing memory problem while providing effective state estimation. Several

differences between the MGS and GS algorithms are examined. An MGS adaptive

filter is derived for a general system and a modal system, with simulation examples

performed using a nonGaussian input signal. The modal system simulation results

are compared to those produced from an augmented Kalman filter based on an

augmented modal system model assuming a narrowband Gaussian input signal. A

necessary condition for effective MGS estimation is derived. Alternate estimation

procedures are developed to compensate for situations when this condition is not

met. Several configurations are simulated and their performance results are

analyzed and compared. Two methods of monitoring and updating key parameters

of the MGS filter are developed. Simulation results are analyzed to investigate the

performance of these methods.

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