Comparative analysis of recursive and nonrecursive linearization-based estimation algorithms Comparative analysis of recursive and nonrecursive linearization-based estimation algorithms

Two schemes of suboptimal estimation algorithms designed with the use of the Bayesian approach and based on the linearization of state vector functions and measurement model are compared. One of these schemes, in which the estimate is calculated recursively with respect to measurements, is tradition...

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Veröffentlicht in:International journal of dynamics and control Jg. 13; H. 2; S. 95
Hauptverfasser: Isaev, Alexey, Stepanov, Oleg, Litvinenko, Yulia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Approximation
Autonomous underwater vehicles
Bayesian analysis
Complexity
Control
Control and Systems Theory
Density
Dynamical Systems
Engineering
Estimates
Kalman filters
Linearization
State vectors
Vibration
Navigation data
Linearization-based algorithms
Comparison
Nonrecursive scheme
Nonlinear filtering
ISSN:2195-268X, 2195-2698
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Abstract Two schemes of suboptimal estimation algorithms designed with the use of the Bayesian approach and based on the linearization of state vector functions and measurement model are compared. One of these schemes, in which the estimate is calculated recursively with respect to measurements, is traditional, and the other one, nonrecursive, involves the use of a full set of all available measurements. It is shown that when solving a special class of problems in which the posteriori density has a complex multi-extremal character at the initial moments of time, but over time it becomes close to the Gaussian one, algorithms designed with the use of a nonrecursive scheme can be effective, in contrast to traditional recursive algorithms using a Gaussian approximation of the posteriori density at each step. Advantages of the nonrecursive algorithms are discussed and illustrated, first, on a simple methodological example and then by solving a practical navigation problem for a group of autonomous underwater vehicles (AUVs).
AbstractList Two schemes of suboptimal estimation algorithms designed with the use of the Bayesian approach and based on the linearization of state vector functions and measurement model are compared. One of these schemes, in which the estimate is calculated recursively with respect to measurements, is traditional, and the other one, nonrecursive, involves the use of a full set of all available measurements. It is shown that when solving a special class of problems in which the posteriori density has a complex multi-extremal character at the initial moments of time, but over time it becomes close to the Gaussian one, algorithms designed with the use of a nonrecursive scheme can be effective, in contrast to traditional recursive algorithms using a Gaussian approximation of the posteriori density at each step. Advantages of the nonrecursive algorithms are discussed and illustrated, first, on a simple methodological example and then by solving a practical navigation problem for a group of autonomous underwater vehicles (AUVs).
ArticleNumber 95
Author Isaev, Alexey
Litvinenko, Yulia
Stepanov, Oleg
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The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
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Linearization-based algorithms
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Nonlinear filtering
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SubjectTerms Accuracy
Algorithms
Approximation
Autonomous underwater vehicles
Bayesian analysis
Complexity
Control
Control and Systems Theory
Density
Dynamical Systems
Engineering
Estimates
Kalman filters
Linearization
State vectors
Vibration
Subtitle Comparative analysis of recursive and nonrecursive linearization-based estimation algorithms
Title Comparative analysis of recursive and nonrecursive linearization-based estimation algorithms
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