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Target tracking with sensor navigation using coupled RSS and AOA measurements

Bibliographic Details
Main Author: Tomic, Slavisa
Publication Date: 2017
Other Authors: Beko, Marko, Dinis, Rui, Gomes, João Pedro
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://doi.org/10.3390/s17112690
Summary: The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions which improved the quality of the paper. This work was partially supported by Fundacao para a Ciencia e a Tecnologia under Project PEst-OE/EEI/UI0066/2014, Project UID/EEA/50008/2013, Project UID/EEA/50009/2013, and Program Investigador FCT under Grant IF/00325/2015.
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Target tracking with sensor navigation using coupled RSS and AOA measurementsAngle of arrival (AoA)Kalman filter (KF)Maximum a posteriori (MAP) estimatorReceived signal strength (RSS)Sensor navigationTarget trackingAnalytical ChemistryAtomic and Molecular Physics, and OpticsBiochemistryInstrumentationElectrical and Electronic EngineeringThe authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions which improved the quality of the paper. This work was partially supported by Fundacao para a Ciencia e a Tecnologia under Project PEst-OE/EEI/UI0066/2014, Project UID/EEA/50008/2013, Project UID/EEA/50009/2013, and Program Investigador FCT under Grant IF/00325/2015.This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine.CTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasDEE2010-A1 TelecomunicaçõesDEE - Departamento de Engenharia Electrotécnica e de ComputadoresRUNTomic, SlavisaBeko, MarkoDinis, RuiGomes, João Pedro2018-07-19T22:11:29Z2017-11-212017-11-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/s17112690eng1424-8220PURE: 4021961http://www.scopus.com/inward/record.url?scp=85035148524&partnerID=8YFLogxKhttps://doi.org/10.3390/s17112690info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:33:58Zoai:run.unl.pt:10362/42035Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:04:52.339792Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Target tracking with sensor navigation using coupled RSS and AOA measurements
title Target tracking with sensor navigation using coupled RSS and AOA measurements
spellingShingle Target tracking with sensor navigation using coupled RSS and AOA measurements
Tomic, Slavisa
Angle of arrival (AoA)
Kalman filter (KF)
Maximum a posteriori (MAP) estimator
Received signal strength (RSS)
Sensor navigation
Target tracking
Analytical Chemistry
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
title_short Target tracking with sensor navigation using coupled RSS and AOA measurements
title_full Target tracking with sensor navigation using coupled RSS and AOA measurements
title_fullStr Target tracking with sensor navigation using coupled RSS and AOA measurements
title_full_unstemmed Target tracking with sensor navigation using coupled RSS and AOA measurements
title_sort Target tracking with sensor navigation using coupled RSS and AOA measurements
author Tomic, Slavisa
author_facet Tomic, Slavisa
Beko, Marko
Dinis, Rui
Gomes, João Pedro
author_role author
author2 Beko, Marko
Dinis, Rui
Gomes, João Pedro
author2_role author
author
author
dc.contributor.none.fl_str_mv CTS - Centro de Tecnologia e Sistemas
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
DEE2010-A1 Telecomunicações
DEE - Departamento de Engenharia Electrotécnica e de Computadores
RUN
dc.contributor.author.fl_str_mv Tomic, Slavisa
Beko, Marko
Dinis, Rui
Gomes, João Pedro
dc.subject.por.fl_str_mv Angle of arrival (AoA)
Kalman filter (KF)
Maximum a posteriori (MAP) estimator
Received signal strength (RSS)
Sensor navigation
Target tracking
Analytical Chemistry
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
topic Angle of arrival (AoA)
Kalman filter (KF)
Maximum a posteriori (MAP) estimator
Received signal strength (RSS)
Sensor navigation
Target tracking
Analytical Chemistry
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering
description The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions which improved the quality of the paper. This work was partially supported by Fundacao para a Ciencia e a Tecnologia under Project PEst-OE/EEI/UI0066/2014, Project UID/EEA/50008/2013, Project UID/EEA/50009/2013, and Program Investigador FCT under Grant IF/00325/2015.
publishDate 2017
dc.date.none.fl_str_mv 2017-11-21
2017-11-21T00:00:00Z
2018-07-19T22:11:29Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://doi.org/10.3390/s17112690
url https://doi.org/10.3390/s17112690
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1424-8220
PURE: 4021961
http://www.scopus.com/inward/record.url?scp=85035148524&partnerID=8YFLogxK
https://doi.org/10.3390/s17112690
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596420383309824

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