Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of Aerospace Technology and Management (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462021000100337 |
Resumo: | ABSTRACT The development and proliferation of small unmanned aerial vehicles (UAVs) have led to the need to create systems for tracking UAVs and monitoring their authorized activities. The presence of electromagnetic radiation makes it possible to use passive radio monitoring systems, based on wireless sensor networks, for tracking UAVs. Methods, based on angle-of-arrival (AOA) measurements, are widely used for determining the location of a radio source using wireless sensor networks. In practice, it becomes necessary to take into account the appearance of abnormal (rough) measurements, which lead to a sharp deterioration in the accuracy characteristics of Kalman filtration algorithms. In this work, to synthesize an optimal adaptive filtering algorithm, the Markov property of a mixed process was used, which includes a continuous-valued vector of UAV movement parameters and discrete parameters that characterize the type of measurements of the sensors of the sensor network. A quasi-optimal algorithm of adaptive filtering of UAV movement parameters when using AOA measurements of the sensor network was obtained using the Gaussian approximation method of the posterior probability density. Its analysis is carried out using a model example. The quasi-optimal adaptive filtering algorithm allows to eliminate the uncontrolled increase of estimates errors of the UAV movement parameters and it does not require significant computational costs. |
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Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal MeasurementsUAV movement parametersAOA measurementsAdaptive algorithmAbnormal measurementsWireless sensor networksABSTRACT The development and proliferation of small unmanned aerial vehicles (UAVs) have led to the need to create systems for tracking UAVs and monitoring their authorized activities. The presence of electromagnetic radiation makes it possible to use passive radio monitoring systems, based on wireless sensor networks, for tracking UAVs. Methods, based on angle-of-arrival (AOA) measurements, are widely used for determining the location of a radio source using wireless sensor networks. In practice, it becomes necessary to take into account the appearance of abnormal (rough) measurements, which lead to a sharp deterioration in the accuracy characteristics of Kalman filtration algorithms. In this work, to synthesize an optimal adaptive filtering algorithm, the Markov property of a mixed process was used, which includes a continuous-valued vector of UAV movement parameters and discrete parameters that characterize the type of measurements of the sensors of the sensor network. A quasi-optimal algorithm of adaptive filtering of UAV movement parameters when using AOA measurements of the sensor network was obtained using the Gaussian approximation method of the posterior probability density. Its analysis is carried out using a model example. The quasi-optimal adaptive filtering algorithm allows to eliminate the uncontrolled increase of estimates errors of the UAV movement parameters and it does not require significant computational costs.Departamento de Ciência e Tecnologia Aeroespacial2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462021000100337Journal of Aerospace Technology and Management v.13 2021reponame:Journal of Aerospace Technology and Management (Online)instname:Departamento de Ciência e Tecnologia Aeroespacial (DCTA)instacron:DCTA10.1590/jatm.v13.1242info:eu-repo/semantics/openAccessZhuk,Serhii YakovychTovkach,Igor OlegovychNeuimin,OleksandrVasyliev,Volodymyreng2021-12-06T00:00:00Zoai:scielo:S2175-91462021000100337Revistahttp://www.jatm.com.br/ONGhttps://old.scielo.br/oai/scielo-oai.php||secretary@jatm.com.br2175-91461984-9648opendoar:2021-12-06T00:00Journal of Aerospace Technology and Management (Online) - Departamento de Ciência e Tecnologia Aeroespacial (DCTA)false |
dc.title.none.fl_str_mv |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
title |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
spellingShingle |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements Zhuk,Serhii Yakovych UAV movement parameters AOA measurements Adaptive algorithm Abnormal measurements Wireless sensor networks |
title_short |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
title_full |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
title_fullStr |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
title_full_unstemmed |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
title_sort |
Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements |
author |
Zhuk,Serhii Yakovych |
author_facet |
Zhuk,Serhii Yakovych Tovkach,Igor Olegovych Neuimin,Oleksandr Vasyliev,Volodymyr |
author_role |
author |
author2 |
Tovkach,Igor Olegovych Neuimin,Oleksandr Vasyliev,Volodymyr |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Zhuk,Serhii Yakovych Tovkach,Igor Olegovych Neuimin,Oleksandr Vasyliev,Volodymyr |
dc.subject.por.fl_str_mv |
UAV movement parameters AOA measurements Adaptive algorithm Abnormal measurements Wireless sensor networks |
topic |
UAV movement parameters AOA measurements Adaptive algorithm Abnormal measurements Wireless sensor networks |
description |
ABSTRACT The development and proliferation of small unmanned aerial vehicles (UAVs) have led to the need to create systems for tracking UAVs and monitoring their authorized activities. The presence of electromagnetic radiation makes it possible to use passive radio monitoring systems, based on wireless sensor networks, for tracking UAVs. Methods, based on angle-of-arrival (AOA) measurements, are widely used for determining the location of a radio source using wireless sensor networks. In practice, it becomes necessary to take into account the appearance of abnormal (rough) measurements, which lead to a sharp deterioration in the accuracy characteristics of Kalman filtration algorithms. In this work, to synthesize an optimal adaptive filtering algorithm, the Markov property of a mixed process was used, which includes a continuous-valued vector of UAV movement parameters and discrete parameters that characterize the type of measurements of the sensors of the sensor network. A quasi-optimal algorithm of adaptive filtering of UAV movement parameters when using AOA measurements of the sensor network was obtained using the Gaussian approximation method of the posterior probability density. Its analysis is carried out using a model example. The quasi-optimal adaptive filtering algorithm allows to eliminate the uncontrolled increase of estimates errors of the UAV movement parameters and it does not require significant computational costs. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462021000100337 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2175-91462021000100337 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/jatm.v13.1242 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Departamento de Ciência e Tecnologia Aeroespacial |
publisher.none.fl_str_mv |
Departamento de Ciência e Tecnologia Aeroespacial |
dc.source.none.fl_str_mv |
Journal of Aerospace Technology and Management v.13 2021 reponame:Journal of Aerospace Technology and Management (Online) instname:Departamento de Ciência e Tecnologia Aeroespacial (DCTA) instacron:DCTA |
instname_str |
Departamento de Ciência e Tecnologia Aeroespacial (DCTA) |
instacron_str |
DCTA |
institution |
DCTA |
reponame_str |
Journal of Aerospace Technology and Management (Online) |
collection |
Journal of Aerospace Technology and Management (Online) |
repository.name.fl_str_mv |
Journal of Aerospace Technology and Management (Online) - Departamento de Ciência e Tecnologia Aeroespacial (DCTA) |
repository.mail.fl_str_mv |
||secretary@jatm.com.br |
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1754732532413235200 |