A Game Theory Approach to Constrained Minimax State Estimation

Daniel J. Simon

doi:10.1109/TSP.2005.861732

A Game Theory Approach to Constrained Minimax State Estimation

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Abstract

This paper presents a game theory approach to the constrained state estimation of linear discrete time dynamic systems. In the application of state estimators, there is often known model or signal information that is either ignored or dealt with heuristically. For example, constraints on the state values (which may be based on physical considerations) are often neglected because they do not easily fit into the structure of the state estimator. This paper develops a method for incorporating state equality constraints into a minimax state estimator. The algorithm is demonstrated on a simple vehicle tracking simulation.

Original language	American English
Journal	IEEE Transactions on Signal Processing
Volume	54
DOIs	https://doi.org/10.1109/TSP.2005.861732
State	Published - Feb 1 2006

Keywords

game theory
H(infinity) filter
minimax filter
state constraints
state estimation

Disciplines

Electrical and Computer Engineering

Access to Document

10.1109/TSP.2005.861732License: CC BY

Cite this

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abstract = "This paper presents a game theory approach to the constrained state estimation of linear discrete time dynamic systems. In the application of state estimators, there is often known model or signal information that is either ignored or dealt with heuristically. For example, constraints on the state values (which may be based on physical considerations) are often neglected because they do not easily fit into the structure of the state estimator. This paper develops a method for incorporating state equality constraints into a minimax state estimator. The algorithm is demonstrated on a simple vehicle tracking simulation.",

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N2 - This paper presents a game theory approach to the constrained state estimation of linear discrete time dynamic systems. In the application of state estimators, there is often known model or signal information that is either ignored or dealt with heuristically. For example, constraints on the state values (which may be based on physical considerations) are often neglected because they do not easily fit into the structure of the state estimator. This paper develops a method for incorporating state equality constraints into a minimax state estimator. The algorithm is demonstrated on a simple vehicle tracking simulation.

AB - This paper presents a game theory approach to the constrained state estimation of linear discrete time dynamic systems. In the application of state estimators, there is often known model or signal information that is either ignored or dealt with heuristically. For example, constraints on the state values (which may be based on physical considerations) are often neglected because they do not easily fit into the structure of the state estimator. This paper develops a method for incorporating state equality constraints into a minimax state estimator. The algorithm is demonstrated on a simple vehicle tracking simulation.

KW - game theory

KW - H(infinity) filter

KW - minimax filter

KW - state constraints

KW - state estimation

UR - http://engagedscholarship.csuohio.edu/sciphysics_facpub/139

U2 - 10.1109/TSP.2005.861732

DO - 10.1109/TSP.2005.861732

M3 - Article

VL - 54

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

ER -

A Game Theory Approach to Constrained Minimax State Estimation

Abstract

Keywords

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