Treffer: Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments

Title:
Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments
Authors:
NEMETH, Christopher, FEARNHEAD, Paul, MIHAYLOVA, Lyudmila
Source:
IEEE transactions on signal processing. 62(5-8):1245-1255
Publisher Information:
New York, NY: Institute of Electrical and Electronics Engineers, 2014.
Publication Year:
2014
Physical Description:
print, 29 ref
Original Material:
INIST-CNRS
Subject Terms:
Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Apprentissage, Learning, Aprendizaje, Cible mobile, Moving target, Blanco móvil, Estimation Bayes, Bayes estimation, Estimación Bayes, Estimation paramètre, Parameter estimation, Estimación parámetro, Estimation état, State estimation, Estimación estado, Méthode Bayes, Bayes methods, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode adaptative, Adaptive method, Método adaptativo, Méthode combinatoire, Combinatorial method, Método combinatorio, Méthode séquentielle, Sequential method, Método secuencial, Poursuite cible, Target tracking, Simulation numérique, Numerical simulation, Simulación numérica, Système non linéaire, Non linear system, Sistema no lineal, Traitement signal, Signal processing, Procesamiento señal, Approche multimodèles, Multiple models approach, Sequential Monte Carlo methods, joint state and parameter estimation, nonlinear systems, particle learning, tracking maneuvering targets
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department Mathematics and Statistics, Lancaster University, Lancaster, Lancashire LA1 4YW, United Kingdom
Department of Automatic Control and Systems Engineering Sheffield University, Sheffield S1 4DT, United Kingdom
ISSN:
1053-587X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28403649
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Telecommunications and information theory
Accession Number:
edscal.28403649
Database:
PASCAL Archive

Weitere Informationen

This paper develops a novel sequential Monte Carlo (SMC) approach for joint state and parameter estimation that can deal efficiently with abruptly changing parameters which is a common case when tracking maneuvering targets. The approach combines Bayesian methods for dealing with change-points with methods for estimating static parameters within the SMC framework. The result is an approach that adaptively estimates the model parameters in accordance with changes to the target's trajectory. The developed approach is compared against the Interacting Multiple Model (IMM) filter for tracking a maneuvering target over a complex maneuvering scenario with nonlinear observations. In the IMM filter a large combination of models is required to account for unknown parameters. In contrast, the proposed approach circumvents the combinatorial complexity of applying multiple models in the IMM filter through Bayesian parameter estimation techniques. The developed approach is validated over complex maneuvering scenarios where both the system parameters and measurement noise parameters are unknown. Accurate estimation results are presented.