A New Generalized Residual Multiple Model Adaptive Estimator of Parameters and States

Authors

Charles D. Ormsby, Air Force Research Laboratory
John F. Raquet, Air Force Institute of Technology
Peter S. Maybeck, Air Force Institute of Technology

Document Type

Article

Publication Date

5-2006

Abstract

This article develops a modification to the standard Multiple Model Adaptive Estimator (MMAE) which allows the use of a new “generalized residual” in the hypothesis conditional probability calculation. The generalized residual is a linear combination of the traditional Kalman filter residual and the “post-fit” Kalman filter residual which is calculated after measurement incorporation. This new modified MMAE is termed a Generalized Residual Multiple Model Adaptive Estimator (GRMMAE). A derivation is provided for the hypothesis conditional probability formula which the GRMMAE uses to calculate probabilities that each elemental filter contains the correct parameter value.
Abstract excerpt © Elsevier

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DOI

10.1016/j.mcm.2005.12.003

Source Publication

Mathematical and Computer Modelling

Recommended Citation

Ormsby, C. D., Raquet, J. F., & Maybeck, P. S. (2006). A new generalized residual multiple model adaptive estimator of parameters and states. Mathematical and Computer Modelling, 43(9–10), 1092–1113. https://doi.org/10.1016/j.mcm.2005.12.003