Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates

This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scat...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Martin Schmidhammer [verfasserIn] Christian Gentner [verfasserIn] Benjamin Siebler [verfasserIn] Stephan Sand [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	mulitlateration localization nonlinear least-squares levenberg–marquardt tracking extended kalman filter bayesian performance bounds posterior cramér–rao lower bound

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 19(2019), 21, p 4802
Übergeordnetes Werk:	volume:19 ; year:2019 ; number:21, p 4802

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s19214802

Katalog-ID:	DOAJ079304362

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520			\|a This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0</mn< <mo<.</mo< <mn<8</mn< </mrow< </semantics< </math< </inline-formula< m at 90% confidence.
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allfieldsGer	10.3390/s19214802 doi (DE-627)DOAJ079304362 (DE-599)DOAJef92654a62364adc8e4e0f98a2667b36 DE-627 ger DE-627 rakwb eng TP1-1185 Martin Schmidhammer verfasserin aut Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0</mn< <mo<.</mo< <mn<8</mn< </mrow< </semantics< </math< </inline-formula< m at 90% confidence. mulitlateration localization nonlinear least-squares levenberg–marquardt tracking extended kalman filter bayesian performance bounds posterior cramér–rao lower bound Chemical technology Christian Gentner verfasserin aut Benjamin Siebler verfasserin aut Stephan Sand verfasserin aut In Sensors MDPI AG, 2003 19(2019), 21, p 4802 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:21, p 4802 https://doi.org/10.3390/s19214802 kostenfrei https://doaj.org/article/ef92654a62364adc8e4e0f98a2667b36 kostenfrei https://www.mdpi.com/1424-8220/19/21/4802 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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 19 2019 21, p 4802
allfieldsSound	10.3390/s19214802 doi (DE-627)DOAJ079304362 (DE-599)DOAJef92654a62364adc8e4e0f98a2667b36 DE-627 ger DE-627 rakwb eng TP1-1185 Martin Schmidhammer verfasserin aut Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0</mn< <mo<.</mo< <mn<8</mn< </mrow< </semantics< </math< </inline-formula< m at 90% confidence. mulitlateration localization nonlinear least-squares levenberg–marquardt tracking extended kalman filter bayesian performance bounds posterior cramér–rao lower bound Chemical technology Christian Gentner verfasserin aut Benjamin Siebler verfasserin aut Stephan Sand verfasserin aut In Sensors MDPI AG, 2003 19(2019), 21, p 4802 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:19 year:2019 number:21, p 4802 https://doi.org/10.3390/s19214802 kostenfrei https://doaj.org/article/ef92654a62364adc8e4e0f98a2667b36 kostenfrei https://www.mdpi.com/1424-8220/19/21/4802 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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 19 2019 21, p 4802
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callnumber-first	T - Technology
author	Martin Schmidhammer
spellingShingle	Martin Schmidhammer misc TP1-1185 misc mulitlateration misc localization misc nonlinear least-squares misc levenberg–marquardt misc tracking misc extended kalman filter misc bayesian performance bounds misc posterior cramér–rao lower bound misc Chemical technology Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
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topic_title	TP1-1185 Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates mulitlateration localization nonlinear least-squares levenberg–marquardt tracking extended kalman filter bayesian performance bounds posterior cramér–rao lower bound
topic	misc TP1-1185 misc mulitlateration misc localization misc nonlinear least-squares misc levenberg–marquardt misc tracking misc extended kalman filter misc bayesian performance bounds misc posterior cramér–rao lower bound misc Chemical technology
topic_unstemmed	misc TP1-1185 misc mulitlateration misc localization misc nonlinear least-squares misc levenberg–marquardt misc tracking misc extended kalman filter misc bayesian performance bounds misc posterior cramér–rao lower bound misc Chemical technology
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title	Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
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title_full	Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
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title_sort	localization and tracking of discrete mobile scatterers in vehicular environments using delay estimates
callnumber	TP1-1185
title_auth	Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
abstract	This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0</mn< <mo<.</mo< <mn<8</mn< </mrow< </semantics< </math< </inline-formula< m at 90% confidence.
abstractGer	This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0</mn< <mo<.</mo< <mn<8</mn< </mrow< </semantics< </math< </inline-formula< m at 90% confidence.
abstract_unstemmed	This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula< <math display="inline"< <semantics< <mrow< <mn<0</mn< <mo<.</mo< <mn<8</mn< </mrow< </semantics< </math< </inline-formula< m at 90% confidence.
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title_short	Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
url	https://doi.org/10.3390/s19214802 https://doaj.org/article/ef92654a62364adc8e4e0f98a2667b36 https://www.mdpi.com/1424-8220/19/21/4802 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T22:47:39.910Z
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