An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter

Abstract The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter t...
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Autor*in:	Chen, Jian [verfasserIn] Song, Shaojing Gong, Yumei Zhang, Shanxin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	An indoor fusion navigation algorithm HV-derivative dynamic time warping Chicken particle filter

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Satellite navigation - Cham : Springer Nature Switzerland AG, 2020, 3(2022), 1 vom: 27. Juni
Übergeordnetes Werk:	volume:3 ; year:2022 ; number:1 ; day:27 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s43020-022-00073-3

Katalog-ID:	SPR047415460

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spelling	10.1186/s43020-022-00073-3 doi (DE-627)SPR047415460 (SPR)s43020-022-00073-3-e DE-627 ger DE-627 rakwb eng Chen, Jian verfasserin (orcid)0000-0003-0987-1665 aut An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly. An indoor fusion navigation algorithm (dpeaa)DE-He213 HV-derivative dynamic time warping (dpeaa)DE-He213 Chicken particle filter (dpeaa)DE-He213 Song, Shaojing aut Gong, Yumei aut Zhang, Shanxin aut Enthalten in Satellite navigation Cham : Springer Nature Switzerland AG, 2020 3(2022), 1 vom: 27. Juni (DE-627)1696030285 (DE-600)3018439-3 2662-1363 nnns volume:3 year:2022 number:1 day:27 month:06 https://dx.doi.org/10.1186/s43020-022-00073-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4392 GBV_ILN_4700 AR 3 2022 1 27 06
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allfieldsSound	10.1186/s43020-022-00073-3 doi (DE-627)SPR047415460 (SPR)s43020-022-00073-3-e DE-627 ger DE-627 rakwb eng Chen, Jian verfasserin (orcid)0000-0003-0987-1665 aut An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly. An indoor fusion navigation algorithm (dpeaa)DE-He213 HV-derivative dynamic time warping (dpeaa)DE-He213 Chicken particle filter (dpeaa)DE-He213 Song, Shaojing aut Gong, Yumei aut Zhang, Shanxin aut Enthalten in Satellite navigation Cham : Springer Nature Switzerland AG, 2020 3(2022), 1 vom: 27. Juni (DE-627)1696030285 (DE-600)3018439-3 2662-1363 nnns volume:3 year:2022 number:1 day:27 month:06 https://dx.doi.org/10.1186/s43020-022-00073-3 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 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_4392 GBV_ILN_4700 AR 3 2022 1 27 06
language	English
source	Enthalten in Satellite navigation 3(2022), 1 vom: 27. Juni volume:3 year:2022 number:1 day:27 month:06
sourceStr	Enthalten in Satellite navigation 3(2022), 1 vom: 27. Juni volume:3 year:2022 number:1 day:27 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	An indoor fusion navigation algorithm HV-derivative dynamic time warping Chicken particle filter
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authorswithroles_txt_mv	Chen, Jian @@aut@@ Song, Shaojing @@aut@@ Gong, Yumei @@aut@@ Zhang, Shanxin @@aut@@
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author	Chen, Jian
spellingShingle	Chen, Jian misc An indoor fusion navigation algorithm misc HV-derivative dynamic time warping misc Chicken particle filter An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter
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topic_title	An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter An indoor fusion navigation algorithm (dpeaa)DE-He213 HV-derivative dynamic time warping (dpeaa)DE-He213 Chicken particle filter (dpeaa)DE-He213
topic	misc An indoor fusion navigation algorithm misc HV-derivative dynamic time warping misc Chicken particle filter
topic_unstemmed	misc An indoor fusion navigation algorithm misc HV-derivative dynamic time warping misc Chicken particle filter
topic_browse	misc An indoor fusion navigation algorithm misc HV-derivative dynamic time warping misc Chicken particle filter
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title	An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter
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title_full	An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter
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author_browse	Chen, Jian Song, Shaojing Gong, Yumei Zhang, Shanxin
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title_sort	indoor fusion navigation algorithm using hv-derivative dynamic time warping and the chicken particle filter
title_auth	An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter
abstract	Abstract The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly. © The Author(s) 2022
abstractGer	Abstract The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly. © The Author(s) 2022
abstract_unstemmed	Abstract The use of dead reckoning and fingerprint matching for navigation is a widespread technical method. However, fingerprint mismatching and low fusion accuracy are prevalent issues in indoor navigation systems. This work presents an improved dynamic time warping and a chicken particle filter to handle these two challenges. To generate the Horizontal and Vertical (HV) fingerprint, the pitch and roll are employed instead of the original fingerprint intensity to extract the horizontal and vertical components of the magnetic field fingerprint. Derivative dynamic time warping employs the HV fingerprint in its derivative form, which receives higher-level features because of the consideration of fingerprint shape information. Chicken Swarm Optimization (CSO) is used to enhance particle weights, which minimizes position error to tackle the particle impoverishment problem for a fusion navigation system. The results of the experiments suggest that the enhanced algorithm can improve indoor navigation accuracy significantly. © The Author(s) 2022
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container_issue	1
title_short	An indoor fusion navigation algorithm using HV-derivative dynamic time warping and the chicken particle filter
url	https://dx.doi.org/10.1186/s43020-022-00073-3
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author2	Song, Shaojing Gong, Yumei Zhang, Shanxin
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up_date	2024-07-04T03:02:52.928Z
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