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Nonlinear estimation techniques applied on target tracking problems / Andrew Gadsden in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 134 N° 5 (Septembre 2012) 

Public ISBD [article]  in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 5 (Septembre 2012) . - 13 p. Titre : Nonlinear estimation techniques applied on target tracking problems Type de document : texte imprimé Auteurs : Andrew Gadsden, Auteur ; Habibi, Saeid, Auteur ; Darcy Dunne, Auteur Année de publication : 2012 Article en page(s) : 13 p. Note générale : Dynamic systems Langues : Anglais (eng) Mots-clés : Estimation  Target  tracking  Kalman  filter  Particle  filter  Smooth  variable  structure  filter  Sliding  mode  estimation Index. décimale : 629.8 Résumé : This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observations. These two target tracking problems provide a good benchmark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its stability and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters performances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accuracy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlinear target tracking problems, which in the past have typically been solved by Kalman or particle filters. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000134000005 [...] [article] Nonlinear estimation techniques applied on target tracking problems [texte imprimé] / Andrew Gadsden, Auteur ; Habibi, Saeid, Auteur ; Darcy Dunne, Auteur . - 2012 . - 13 p. Dynamic systems Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 5 (Septembre 2012) . - 13 p. Mots-clés : Estimation  Target  tracking  Kalman  filter  Particle  filter  Smooth  variable  structure  filter  Sliding  mode  estimation Index. décimale : 629.8 Résumé : This paper discusses the application of four nonlinear estimation techniques on two benchmark target tracking problems. The first problem is a generic air traffic control (ATC) scenario, which involves nonlinear system equations with linear measurements. The second study is a classical ground surveillance problem, where a moving airborne platform with a sensor is used to track a moving target. The tracking scenario is set in two dimensions, with the measurement providing nonlinear bearing-only observations. These two target tracking problems provide a good benchmark for comparing the following nonlinear estimation techniques: the common extended and unscented Kalman filters (EKF/UKF), the particle filter (PF), and the relatively new smooth variable structure filter (SVSF). The results of applying the SVSF on the two target tracking problems demonstrate its stability and robustness. Both of these attributes make use of the SVSF advantageous over other popular methods. The filters performances are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, and tracking accuracy and computational complexity. The purpose of this paper is to demonstrate the effectiveness of applying the SVSF on nonlinear target tracking problems, which in the past have typically been solved by Kalman or particle filters. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000134000005 [...]