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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of dynamics and control Ročník 13; číslo 2; s. 95
Hlavní autori: Isaev, Alexey, Stepanov, Oleg, Litvinenko, Yulia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2025
Springer Nature B.V
Predmet:
ISSN:2195-268X, 2195-2698
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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).
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2195-268X
2195-2698
DOI:10.1007/s40435-025-01592-y