Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation

In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to position...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sun, Tian [verfasserIn] Peng, Ao [verfasserIn] Shi, Jianghong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning

Übergeordnetes Werk:	Enthalten in: Digital signal processing - Orlando, Fla. : Academic Press, 1991, 146
Übergeordnetes Werk:	volume:146

DOI / URN:	10.1016/j.dsp.2023.104372

Katalog-ID:	ELV066973139

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245	1	0	\|a Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation
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520			\|a In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.
650		4	\|a 5G indoor positioning
650		4	\|a Maximum a posterior-penalty function
650		4	\|a Multipath-assisted positioning
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700	1		\|a Shi, Jianghong \|e verfasserin \|4 aut
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allfields	10.1016/j.dsp.2023.104372 doi (DE-627)ELV066973139 (ELSEVIER)S1051-2004(23)00467-0 DE-627 ger DE-627 rda eng 620 VZ 53.73 bkl Sun, Tian verfasserin (orcid)0000-0003-3886-6881 aut Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE. 5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning Peng, Ao verfasserin (orcid)0000-0003-3348-4358 aut Shi, Jianghong verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 146 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:146 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.73 Nachrichtenübertragung VZ AR 146
spelling	10.1016/j.dsp.2023.104372 doi (DE-627)ELV066973139 (ELSEVIER)S1051-2004(23)00467-0 DE-627 ger DE-627 rda eng 620 VZ 53.73 bkl Sun, Tian verfasserin (orcid)0000-0003-3886-6881 aut Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE. 5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning Peng, Ao verfasserin (orcid)0000-0003-3348-4358 aut Shi, Jianghong verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 146 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:146 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.73 Nachrichtenübertragung VZ AR 146
allfields_unstemmed	10.1016/j.dsp.2023.104372 doi (DE-627)ELV066973139 (ELSEVIER)S1051-2004(23)00467-0 DE-627 ger DE-627 rda eng 620 VZ 53.73 bkl Sun, Tian verfasserin (orcid)0000-0003-3886-6881 aut Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE. 5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning Peng, Ao verfasserin (orcid)0000-0003-3348-4358 aut Shi, Jianghong verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 146 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:146 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.73 Nachrichtenübertragung VZ AR 146
allfieldsGer	10.1016/j.dsp.2023.104372 doi (DE-627)ELV066973139 (ELSEVIER)S1051-2004(23)00467-0 DE-627 ger DE-627 rda eng 620 VZ 53.73 bkl Sun, Tian verfasserin (orcid)0000-0003-3886-6881 aut Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE. 5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning Peng, Ao verfasserin (orcid)0000-0003-3348-4358 aut Shi, Jianghong verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 146 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:146 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.73 Nachrichtenübertragung VZ AR 146
allfieldsSound	10.1016/j.dsp.2023.104372 doi (DE-627)ELV066973139 (ELSEVIER)S1051-2004(23)00467-0 DE-627 ger DE-627 rda eng 620 VZ 53.73 bkl Sun, Tian verfasserin (orcid)0000-0003-3886-6881 aut Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE. 5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning Peng, Ao verfasserin (orcid)0000-0003-3348-4358 aut Shi, Jianghong verfasserin aut Enthalten in Digital signal processing Orlando, Fla. : Academic Press, 1991 146 Online-Ressource (DE-627)254910319 (DE-600)1463243-3 (DE-576)114818002 1051-2004 nnns volume:146 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 53.73 Nachrichtenübertragung VZ AR 146
language	English
source	Enthalten in Digital signal processing 146 volume:146
sourceStr	Enthalten in Digital signal processing 146 volume:146
format_phy_str_mv	Article
bklname	Nachrichtenübertragung
institution	findex.gbv.de
topic_facet	5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning
dewey-raw	620
isfreeaccess_bool	false
container_title	Digital signal processing
authorswithroles_txt_mv	Sun, Tian @@aut@@ Peng, Ao @@aut@@ Shi, Jianghong @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	254910319
dewey-sort	3620
id	ELV066973139
language_de	englisch
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author	Sun, Tian
spellingShingle	Sun, Tian ddc 620 bkl 53.73 misc 5G indoor positioning misc Maximum a posterior-penalty function misc Multipath-assisted positioning Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation
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topic_title	620 VZ 53.73 bkl Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation 5G indoor positioning Maximum a posterior-penalty function Multipath-assisted positioning
topic	ddc 620 bkl 53.73 misc 5G indoor positioning misc Maximum a posterior-penalty function misc Multipath-assisted positioning
topic_unstemmed	ddc 620 bkl 53.73 misc 5G indoor positioning misc Maximum a posterior-penalty function misc Multipath-assisted positioning
topic_browse	ddc 620 bkl 53.73 misc 5G indoor positioning misc Maximum a posterior-penalty function misc Multipath-assisted positioning
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title	Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation
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title_full	Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation
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title_sort	multipath-assisted positioning enhancement via dc-slam with map-pf: an in-depth analysis and performance evaluation
title_auth	Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation
abstract	In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.
abstractGer	In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.
abstract_unstemmed	In indoor environments, traditional 5G positioning methods, such as trilateration and triangulation, encounter Non-Line-Of-Sight (NLOS) interference, significantly reducing the positioning accuracy. Notably, the quasi-static indoor channel offers valuable environmental features conducive to positioning. We present the newly developed Deep Coupling-Simultaneous Localization And Mapping (DC-SLAM) method that utilizes specular reflection multipath. DC-SLAM jointly estimates the delay and angle of multipath components, as well as the positions of mirror Virtual Anchors (VAs) and User Equipment (UE). Using raw sensor data, specifically channel state information sequences as observations, DC-SLAM integrates the Space Alternating Generalized Expectation Maximization (SAGE) with Bayesian filtering into a single algorithm employing the Maximum A Posteriori Probability-Penalty Function (MAP-PF) approach. In multipath signal processing, the penalty function serves as a prior distribution, guiding the algorithm to find a likelihood function peak closely aligned with the tracker-estimated delay and angle. In Bayesian filtering, it functions as the observational likelihood, updating information on predicted positions of UE and VAs. We carried out a numerical simulation experiment in an office setting. Results indicate that the accuracy of multipath delay and angle determined by MAP-PF generally surpasses that of pure SAGE. Furthermore, the precision of positions for VAs and UE derived from MAP-PF markedly exceeds those from belief propagation-SLAM, a non-joint estimation approach. Optimally, DC-SLAM achieves an average positioning accuracy of 0.11 m in a 2D plane for UE.
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title_short	Multipath-assisted positioning enhancement via DC-SLAM with MAP-PF: An in-depth analysis and performance evaluation
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doi_str	10.1016/j.dsp.2023.104372
up_date	2024-07-06T19:38:39.876Z
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