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dc.contributor.author
Huang, Zenghong
dc.contributor.author
Marelli, Damian Edgardo
dc.contributor.author
Xu, Yong
dc.contributor.author
Fu, Minyue
dc.date.available
2022-11-29T12:01:20Z
dc.date.issued
2021-12
dc.identifier.citation
Huang, Zenghong; Marelli, Damian Edgardo; Xu, Yong; Fu, Minyue; Distributed Target Tracking Using Maximum Likelihood Kalman Filter with Non-Linear Measurements; Institute of Electrical and Electronics Engineers; IEEE Sensors Journal; 21; 24; 12-2021; 27818-27826
dc.identifier.issn
1530-437X
dc.identifier.uri
http://hdl.handle.net/11336/179336
dc.description.abstract
We propose a distributed method for tracking a target with linear dynamics and non-linear measurements acquired by a number of sensors. The proposed method is based on a Bayesian tracking technique called maximum likelihood Kalman filter (MLKF), which is known to be asymptotically optimal, in the mean squared sense, as the number of sensors becomes large. This method requires, at each time step, the solution of a maximum likelihood (ML) estimation problem as well as the Hessian matrix of the likelihood function at the optimal. In order to obtain a distributed method, we compute the ML estimate using a recently proposed fully distributed optimization method, which yields the required Hessian matrix as a byproduct of the optimization procedure. We call the algorithm so obtained the distributed MLKF (DMLKF). Numerical simulation results show that DMLKF largely outperforms other available distributed tracking methods, in terms of tracking accuracy, and that it asymptotically approximates the optimal Bayesian tracking solution, as the number of sensors and inter-node information fusion iterations increase.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BAYESIAN TRACKING
dc.subject
MAXIMUM LIKELIHOOD ESTIMATION
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TARGET TRACKING
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WIRELESS SENSOR NETWORKS
dc.subject.classification
Control Automático y Robótica
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Distributed Target Tracking Using Maximum Likelihood Kalman Filter with Non-Linear Measurements
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-08-31T14:58:29Z
dc.journal.volume
21
dc.journal.number
24
dc.journal.pagination
27818-27826
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Huang, Zenghong. Guangdong University of Technology; China
dc.description.fil
Fil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina
dc.description.fil
Fil: Xu, Yong. Guangdong University of Technology; China
dc.description.fil
Fil: Fu, Minyue. Universidad de Newcastle; Australia
dc.journal.title
IEEE Sensors Journal
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/JSEN.2021.3125153
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/9599711
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