Target tracking using interacting multiple models with particle filtering
- Autores
- Corral, Alberto Mariano; Cernuschi Frías, Bruno
- Año de publicación
- 2011
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The problem of modeling accuracy in target tracking has been well studied in the past and is specially important when tracking maneuvering targets. One of the most simple and elegant ways of improving an algorithm in this sense is by using Interacting Multiple Model (IMM). IMM is a method that takes into account more than one model at the same time. This paper describes how it works and how it has been incorporated in tracking algorithms in the past, specially in the Extended Kalman Filter (EKF). We also introduce a novel way of using it with Particle Filters (PF). The original proposal found here is that we estimate the whole target state sampling particles from the Optimal Function.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
Target Tracking Using
Models - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125338
Ver los metadatos del registro completo
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Target tracking using interacting multiple models with particle filteringCorral, Alberto MarianoCernuschi Frías, BrunoCiencias InformáticasTarget Tracking UsingModelsThe problem of modeling accuracy in target tracking has been well studied in the past and is specially important when tracking maneuvering targets. One of the most simple and elegant ways of improving an algorithm in this sense is by using Interacting Multiple Model (IMM). IMM is a method that takes into account more than one model at the same time. This paper describes how it works and how it has been incorporated in tracking algorithms in the past, specially in the Extended Kalman Filter (EKF). We also introduce a novel way of using it with Particle Filters (PF). The original proposal found here is that we estimate the whole target state sampling particles from the Optimal Function.Sociedad Argentina de Informática e Investigación Operativa2011-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf216-227http://sedici.unlp.edu.ar/handle/10915/125338enginfo:eu-repo/semantics/altIdentifier/issn/1850-2806info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-17T10:12:53Zoai:sedici.unlp.edu.ar:10915/125338Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 10:12:54.029SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
Target tracking using interacting multiple models with particle filtering |
| title |
Target tracking using interacting multiple models with particle filtering |
| spellingShingle |
Target tracking using interacting multiple models with particle filtering Corral, Alberto Mariano Ciencias Informáticas Target Tracking Using Models |
| title_short |
Target tracking using interacting multiple models with particle filtering |
| title_full |
Target tracking using interacting multiple models with particle filtering |
| title_fullStr |
Target tracking using interacting multiple models with particle filtering |
| title_full_unstemmed |
Target tracking using interacting multiple models with particle filtering |
| title_sort |
Target tracking using interacting multiple models with particle filtering |
| dc.creator.none.fl_str_mv |
Corral, Alberto Mariano Cernuschi Frías, Bruno |
| author |
Corral, Alberto Mariano |
| author_facet |
Corral, Alberto Mariano Cernuschi Frías, Bruno |
| author_role |
author |
| author2 |
Cernuschi Frías, Bruno |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas Target Tracking Using Models |
| topic |
Ciencias Informáticas Target Tracking Using Models |
| dc.description.none.fl_txt_mv |
The problem of modeling accuracy in target tracking has been well studied in the past and is specially important when tracking maneuvering targets. One of the most simple and elegant ways of improving an algorithm in this sense is by using Interacting Multiple Model (IMM). IMM is a method that takes into account more than one model at the same time. This paper describes how it works and how it has been incorporated in tracking algorithms in the past, specially in the Extended Kalman Filter (EKF). We also introduce a novel way of using it with Particle Filters (PF). The original proposal found here is that we estimate the whole target state sampling particles from the Optimal Function. Sociedad Argentina de Informática e Investigación Operativa |
| description |
The problem of modeling accuracy in target tracking has been well studied in the past and is specially important when tracking maneuvering targets. One of the most simple and elegant ways of improving an algorithm in this sense is by using Interacting Multiple Model (IMM). IMM is a method that takes into account more than one model at the same time. This paper describes how it works and how it has been incorporated in tracking algorithms in the past, specially in the Extended Kalman Filter (EKF). We also introduce a novel way of using it with Particle Filters (PF). The original proposal found here is that we estimate the whole target state sampling particles from the Optimal Function. |
| publishDate |
2011 |
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2011-08 |
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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/125338 |
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eng |
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eng |
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info:eu-repo/semantics/altIdentifier/issn/1850-2806 |
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