Likelihood Gradient Evaluation Using Square-Root Covariance Filters

Using the array form of numerically stable square-root implementation methods for Kalman filtering formulas, we construct a new square-root algorithm for the log-likelihood gradient (score) evaluation. This avoids the use of the conventional Kalman filter with its inherent numerical instabilities an...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 54; no. 3; pp. 646 - 651
Main Author: Kulikova, M.V.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.2009
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Arrays
Computer science; control theory; systems
Control theory. Systems
Covariance
Covariance matrix
Exact sciences and technology
Filtering algorithms
Gradient methods
identification
Instability
Kalman filtering
Kalman filters
Mathematical models
Maximum likelihood estimation
Modelling and identification
Numerical stability
Riccati equations
Robustness
Roundoff errors
Stability
Stochastic systems
Time measurement
Gradient methods
identification
Covariance
Kalman filter
Rounding error
Robustness
Kalman filtering
System identification
Maximum likelihood
Gradient method
maximum likelihood estimation
Numerical stability
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Description
Summary:Using the array form of numerically stable square-root implementation methods for Kalman filtering formulas, we construct a new square-root algorithm for the log-likelihood gradient (score) evaluation. This avoids the use of the conventional Kalman filter with its inherent numerical instabilities and improves the robustness of computations against roundoff errors. The new algorithm is developed in terms of covariance quantities and based on the ldquocondensed formrdquo of the array square-root filter.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2008.2010989