Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements

T he 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...
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Gespeichert in:

Autor*in:	Serhii Yakovych Zhuk [verfasserIn] Igor Olegovych Tovkach [verfasserIn] Oleksandr Neuimin [verfasserIn] Volodymyr Vasyliev [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	uav movement parameters aoa measurements adaptive algorithm abnormal measurements wireless sensor networks

Übergeordnetes Werk:	In: Journal of Aerospace Technology and Management - Instituto de Aeronáutica e Espaço (IAE), 2010, 13(2021), 1, Seite e4421-e4421
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:1 ; pages:e4421-e4421

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ035447346

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520			\|a T he 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 quasioptimal 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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spelling	(DE-627)DOAJ035447346 (DE-599)DOAJd0f7fc6805a24db3b2b9d6f1914c9c70 DE-627 ger DE-627 rakwb eng TL1-4050 Serhii Yakovych Zhuk verfasserin aut Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier T he 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 quasioptimal 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. uav movement parameters aoa measurements adaptive algorithm abnormal measurements wireless sensor networks Technology T Motor vehicles. Aeronautics. Astronautics Igor Olegovych Tovkach verfasserin aut Oleksandr Neuimin verfasserin aut Volodymyr Vasyliev verfasserin aut In Journal of Aerospace Technology and Management Instituto de Aeronáutica e Espaço (IAE), 2010 13(2021), 1, Seite e4421-e4421 (DE-627)633082236 (DE-600)2567333-6 21759146 nnns volume:13 year:2021 number:1 pages:e4421-e4421 https://doi.org/10.1590/jatm.v13.1242 kostenfrei https://doaj.org/article/d0f7fc6805a24db3b2b9d6f1914c9c70 kostenfrei https://www.scielo.br/j/jatm/a/CBkvqgLVNtL6FDGzfR3399v/?lang=en kostenfrei https://doaj.org/toc/1984-9648 Journal toc kostenfrei https://doaj.org/toc/2175-9146 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 e4421-e4421
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allfieldsGer	(DE-627)DOAJ035447346 (DE-599)DOAJd0f7fc6805a24db3b2b9d6f1914c9c70 DE-627 ger DE-627 rakwb eng TL1-4050 Serhii Yakovych Zhuk verfasserin aut Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier T he 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 quasioptimal 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. uav movement parameters aoa measurements adaptive algorithm abnormal measurements wireless sensor networks Technology T Motor vehicles. Aeronautics. Astronautics Igor Olegovych Tovkach verfasserin aut Oleksandr Neuimin verfasserin aut Volodymyr Vasyliev verfasserin aut In Journal of Aerospace Technology and Management Instituto de Aeronáutica e Espaço (IAE), 2010 13(2021), 1, Seite e4421-e4421 (DE-627)633082236 (DE-600)2567333-6 21759146 nnns volume:13 year:2021 number:1 pages:e4421-e4421 https://doi.org/10.1590/jatm.v13.1242 kostenfrei https://doaj.org/article/d0f7fc6805a24db3b2b9d6f1914c9c70 kostenfrei https://www.scielo.br/j/jatm/a/CBkvqgLVNtL6FDGzfR3399v/?lang=en kostenfrei https://doaj.org/toc/1984-9648 Journal toc kostenfrei https://doaj.org/toc/2175-9146 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 e4421-e4421
allfieldsSound	(DE-627)DOAJ035447346 (DE-599)DOAJd0f7fc6805a24db3b2b9d6f1914c9c70 DE-627 ger DE-627 rakwb eng TL1-4050 Serhii Yakovych Zhuk verfasserin aut Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier T he 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 quasioptimal 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. uav movement parameters aoa measurements adaptive algorithm abnormal measurements wireless sensor networks Technology T Motor vehicles. Aeronautics. Astronautics Igor Olegovych Tovkach verfasserin aut Oleksandr Neuimin verfasserin aut Volodymyr Vasyliev verfasserin aut In Journal of Aerospace Technology and Management Instituto de Aeronáutica e Espaço (IAE), 2010 13(2021), 1, Seite e4421-e4421 (DE-627)633082236 (DE-600)2567333-6 21759146 nnns volume:13 year:2021 number:1 pages:e4421-e4421 https://doi.org/10.1590/jatm.v13.1242 kostenfrei https://doaj.org/article/d0f7fc6805a24db3b2b9d6f1914c9c70 kostenfrei https://www.scielo.br/j/jatm/a/CBkvqgLVNtL6FDGzfR3399v/?lang=en kostenfrei https://doaj.org/toc/1984-9648 Journal toc kostenfrei https://doaj.org/toc/2175-9146 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 1 e4421-e4421
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callnumber-first	T - Technology
author	Serhii Yakovych Zhuk
spellingShingle	Serhii Yakovych Zhuk misc TL1-4050 misc uav movement parameters misc aoa measurements misc adaptive algorithm misc abnormal measurements misc wireless sensor networks misc Technology misc T misc Motor vehicles. Aeronautics. Astronautics Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements
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topic_title	TL1-4050 Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements uav movement parameters aoa measurements adaptive algorithm abnormal measurements wireless sensor networks
topic	misc TL1-4050 misc uav movement parameters misc aoa measurements misc adaptive algorithm misc abnormal measurements misc wireless sensor networks misc Technology misc T misc Motor vehicles. Aeronautics. Astronautics
topic_unstemmed	misc TL1-4050 misc uav movement parameters misc aoa measurements misc adaptive algorithm misc abnormal measurements misc wireless sensor networks misc Technology misc T misc Motor vehicles. Aeronautics. Astronautics
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title	Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements
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title_full	Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements
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title_sort	adaptive filtering of uav movement parameters based on aoa-measurements of the sensor network in the presence of abnormal measurements
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title_auth	Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements
abstract	T he 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 quasioptimal 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.
abstractGer	T he 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 quasioptimal 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.
abstract_unstemmed	T he 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 quasioptimal 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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title_short	Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements
url	https://doi.org/10.1590/jatm.v13.1242 https://doaj.org/article/d0f7fc6805a24db3b2b9d6f1914c9c70 https://www.scielo.br/j/jatm/a/CBkvqgLVNtL6FDGzfR3399v/?lang=en https://doaj.org/toc/1984-9648 https://doaj.org/toc/2175-9146
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author2	Igor Olegovych Tovkach Oleksandr Neuimin Volodymyr Vasyliev
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up_date	2024-07-03T15:02:16.410Z
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Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements

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