An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning

The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimati...
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Autor*in:	Jingmin Yang [verfasserIn] Shanghui Deng [verfasserIn] Minmin Lin [verfasserIn] Li Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 15, p 5689
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:15, p 5689

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22155689

Katalog-ID:	DOAJ085004243

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allfields	10.3390/s22155689 doi (DE-627)DOAJ085004243 (DE-599)DOAJ48e674b29d1044938a9ed6d2f90a4868 DE-627 ger DE-627 rakwb eng TP1-1185 Jingmin Yang verfasserin aut An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication Chemical technology Shanghui Deng verfasserin aut Minmin Lin verfasserin aut Li Xu verfasserin aut In Sensors MDPI AG, 2003 22(2022), 15, p 5689 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:15, p 5689 https://doi.org/10.3390/s22155689 kostenfrei https://doaj.org/article/48e674b29d1044938a9ed6d2f90a4868 kostenfrei https://www.mdpi.com/1424-8220/22/15/5689 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 22 2022 15, p 5689
spelling	10.3390/s22155689 doi (DE-627)DOAJ085004243 (DE-599)DOAJ48e674b29d1044938a9ed6d2f90a4868 DE-627 ger DE-627 rakwb eng TP1-1185 Jingmin Yang verfasserin aut An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication Chemical technology Shanghui Deng verfasserin aut Minmin Lin verfasserin aut Li Xu verfasserin aut In Sensors MDPI AG, 2003 22(2022), 15, p 5689 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:15, p 5689 https://doi.org/10.3390/s22155689 kostenfrei https://doaj.org/article/48e674b29d1044938a9ed6d2f90a4868 kostenfrei https://www.mdpi.com/1424-8220/22/15/5689 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 22 2022 15, p 5689
allfields_unstemmed	10.3390/s22155689 doi (DE-627)DOAJ085004243 (DE-599)DOAJ48e674b29d1044938a9ed6d2f90a4868 DE-627 ger DE-627 rakwb eng TP1-1185 Jingmin Yang verfasserin aut An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication Chemical technology Shanghui Deng verfasserin aut Minmin Lin verfasserin aut Li Xu verfasserin aut In Sensors MDPI AG, 2003 22(2022), 15, p 5689 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:15, p 5689 https://doi.org/10.3390/s22155689 kostenfrei https://doaj.org/article/48e674b29d1044938a9ed6d2f90a4868 kostenfrei https://www.mdpi.com/1424-8220/22/15/5689 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 22 2022 15, p 5689
allfieldsGer	10.3390/s22155689 doi (DE-627)DOAJ085004243 (DE-599)DOAJ48e674b29d1044938a9ed6d2f90a4868 DE-627 ger DE-627 rakwb eng TP1-1185 Jingmin Yang verfasserin aut An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication Chemical technology Shanghui Deng verfasserin aut Minmin Lin verfasserin aut Li Xu verfasserin aut In Sensors MDPI AG, 2003 22(2022), 15, p 5689 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:15, p 5689 https://doi.org/10.3390/s22155689 kostenfrei https://doaj.org/article/48e674b29d1044938a9ed6d2f90a4868 kostenfrei https://www.mdpi.com/1424-8220/22/15/5689 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 22 2022 15, p 5689
allfieldsSound	10.3390/s22155689 doi (DE-627)DOAJ085004243 (DE-599)DOAJ48e674b29d1044938a9ed6d2f90a4868 DE-627 ger DE-627 rakwb eng TP1-1185 Jingmin Yang verfasserin aut An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively. adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication Chemical technology Shanghui Deng verfasserin aut Minmin Lin verfasserin aut Li Xu verfasserin aut In Sensors MDPI AG, 2003 22(2022), 15, p 5689 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:15, p 5689 https://doi.org/10.3390/s22155689 kostenfrei https://doaj.org/article/48e674b29d1044938a9ed6d2f90a4868 kostenfrei https://www.mdpi.com/1424-8220/22/15/5689 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 22 2022 15, p 5689
language	English
source	In Sensors 22(2022), 15, p 5689 volume:22 year:2022 number:15, p 5689
sourceStr	In Sensors 22(2022), 15, p 5689 volume:22 year:2022 number:15, p 5689
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Jingmin Yang @@aut@@ Shanghui Deng @@aut@@ Minmin Lin @@aut@@ Li Xu @@aut@@
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callnumber-first	T - Technology
author	Jingmin Yang
spellingShingle	Jingmin Yang misc TP1-1185 misc adaptive calibration misc error correction linear regression equation misc indoor ranging and positioning misc logarithmic distance path loss model misc received signal strength indication misc Chemical technology An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning
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topic_title	TP1-1185 An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning adaptive calibration error correction linear regression equation indoor ranging and positioning logarithmic distance path loss model received signal strength indication
topic	misc TP1-1185 misc adaptive calibration misc error correction linear regression equation misc indoor ranging and positioning misc logarithmic distance path loss model misc received signal strength indication misc Chemical technology
topic_unstemmed	misc TP1-1185 misc adaptive calibration misc error correction linear regression equation misc indoor ranging and positioning misc logarithmic distance path loss model misc received signal strength indication misc Chemical technology
topic_browse	misc TP1-1185 misc adaptive calibration misc error correction linear regression equation misc indoor ranging and positioning misc logarithmic distance path loss model misc received signal strength indication misc Chemical technology
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title	An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning
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title_full	An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning
author_sort	Jingmin Yang
journal	Sensors
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author_browse	Jingmin Yang Shanghui Deng Minmin Lin Li Xu
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title_sort	adaptive calibration algorithm based on rssi and ldplm for indoor ranging and positioning
callnumber	TP1-1185
title_auth	An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning
abstract	The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively.
abstractGer	The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively.
abstract_unstemmed	The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively.
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container_issue	15, p 5689
title_short	An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning
url	https://doi.org/10.3390/s22155689 https://doaj.org/article/48e674b29d1044938a9ed6d2f90a4868 https://www.mdpi.com/1424-8220/22/15/5689 https://doaj.org/toc/1424-8220
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doi_str	10.3390/s22155689
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up_date	2024-07-04T01:27:00.758Z
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An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning

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