A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 11; číslo 11; s. 1293
Hlavní autoři:	Wang, Rongrong, Li, Zhaohui, Luo, Haiyong, Zhao, Fang, Shao, Wenhua, Wang, Qu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.06.2019
Témata:	Accuracy Algorithms Artificial intelligence Communications networks Data collection Datasets Deep learning Feature extraction Fingerprints Indoor positioning International conferences Localization Location based services Machine learning multi-layer perceptron Multilayer perceptrons Neural networks Noise reduction Position (location) Regression analysis Regression models regression positioning Remote sensing Robustness stacked denoising autoencoder Teaching methods Training Wireless networks Beijing China New York United States > US China
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset.
AbstractList	With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset.
Author	Zhao, Fang Wang, Qu Wang, Rongrong Li, Zhaohui Luo, Haiyong Shao, Wenhua
Author_xml	– sequence: 1 givenname: Rongrong orcidid: 0000-0002-6916-4332 surname: Wang fullname: Wang, Rongrong – sequence: 2 givenname: Zhaohui surname: Li fullname: Li, Zhaohui – sequence: 3 givenname: Haiyong orcidid: 0000-0001-6827-4225 surname: Luo fullname: Luo, Haiyong – sequence: 4 givenname: Fang surname: Zhao fullname: Zhao, Fang – sequence: 5 givenname: Wenhua surname: Shao fullname: Shao, Wenhua – sequence: 6 givenname: Qu orcidid: 0000-0001-6551-6807 surname: Wang fullname: Wang, Qu
BookMark	eNptUctOIzEQtBBIPC98gaW9Ic3i12TGxwjIghQE4iGOlsfuBIfBDrbnkL_HIWhBiL60u1Suru7eR9s-eEDomJK_nEtyGhNdB5N8C-0x0rBKMMm2v7130VFKC1KCcyqJ2ENhjO9CN6SMn1w1cXji_BziMjqf8W1ILrvgC4TH_TxEl59f8WNa1_dZmxew-Bx8cB_IeMgBvAkWItbe4uuhz66a6lWpbyEaWOYY_CHamek-wdFnPkCPk4uHs8tqevPv6mw8rYxgTa6YMFZ3UlvRMk6krZsZGEPpaKSBWTnirKPciK7gbS2ttrRua8uBSGo6ww0_QFcbXRv0QpV5XnVcqaCd-gBCnCsdszM9qKbprAXRcUprwTnotiatbpkdkZoUA0Xrz0ZrGcPbACmrRRiiL_ZVMSeI4KKVhXWyYZkYUoow-9-VErW-j_q6TyGTH2Tjsl4vO0ft-t--vAN9kJQr
CitedBy_id	crossref_primary_10_1109_ACCESS_2021_3111083 crossref_primary_10_3390_su13031183 crossref_primary_10_1109_JIOT_2022_3232817 crossref_primary_10_1109_LCOMM_2023_3272972 crossref_primary_10_3390_ijgi9040267 crossref_primary_10_1109_JIOT_2024_3484456 crossref_primary_10_1109_ACCESS_2019_2950728 crossref_primary_10_1109_JSEN_2022_3147309 crossref_primary_10_1109_JSEN_2022_3213836 crossref_primary_10_3390_su14074204 crossref_primary_10_1080_17489725_2020_1817582 crossref_primary_10_3390_s20010133 crossref_primary_10_3390_electronics8090989 crossref_primary_10_3390_electronics13224448 crossref_primary_10_3390_electronics14142807 crossref_primary_10_1016_j_dte_2024_100020 crossref_primary_10_1109_JIOT_2024_3521084 crossref_primary_10_3390_s22124622 crossref_primary_10_1016_j_future_2023_02_017 crossref_primary_10_1016_j_eswa_2024_123389 crossref_primary_10_1109_ACCESS_2022_3226816 crossref_primary_10_1109_JIOT_2020_3016146 crossref_primary_10_3390_s25072330 crossref_primary_10_1109_JSEN_2022_3153610 crossref_primary_10_1109_TIM_2020_2998645 crossref_primary_10_3390_rs13061106 crossref_primary_10_1109_ACCESS_2020_3026615 crossref_primary_10_1109_JIOT_2021_3114373 crossref_primary_10_1016_j_measurement_2023_112813 crossref_primary_10_1109_ACCESS_2020_2973212 crossref_primary_10_1016_j_eswa_2021_116455 crossref_primary_10_1108_SR_05_2024_0484 crossref_primary_10_1109_ACCESS_2024_3367356 crossref_primary_10_3390_s20061691 crossref_primary_10_1080_24751839_2021_1975425 crossref_primary_10_1109_ACCESS_2021_3095546
Cites_doi	10.1109/WIMOB.2007.4390815 10.1145/1390156.1390294 10.1109/CANDARW.2018.00045 10.1155/2016/1945695 10.1109/ACCESS.2018.2884193 10.1109/JSEN.2018.2885958 10.3390/rs11030294 10.1177/1550147718815637 10.1109/VTCFall.2018.8690989 10.1109/TMC.2013.29 10.1109/ACCESS.2017.2728789 10.1214/aos/1013203451 10.1109/TMC.2007.1025 10.3390/s17112678 10.1109/ICMLA.2017.0-185 10.1109/ISDFS.2018.8355353 10.1109/IPIN.2016.7743700 10.1145/2939672.2939785 10.1007/978-3-319-54042-9_57 10.1109/IPIN.2016.7743678 10.1109/CCDC.2016.7532172 10.1109/COMST.2015.2464084 10.3390/rs11060652 10.1109/SMC.2014.6974105 10.1145/1859995.1860016 10.1109/IPIN.2017.8115943 10.1201/b12207 10.3390/rs11050566 10.1109/ICC.2018.8422562 10.1109/ComComAp.2014.7017163 10.1109/WoWMoM.2017.7974313 10.1109/ICC.2017.7997235 10.1109/FOAN.2017.8215259 10.3390/s18103317 10.1109/LSENS.2017.2787651 10.1145/2462456.2464463 10.1007/BF02551274 10.1109/TVT.2018.2870160 10.1177/1550147718758263 10.1109/WCNC.2007.392 10.1109/ACCESS.2017.2720164
ContentType	Journal Article
Copyright	2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS DOA
DOI	10.3390/rs11111293
DatabaseName	CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection (via ProQuest) Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Engineering Collection DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography
EISSN	2072-4292
ExternalDocumentID	oai_doaj_org_article_77bdde4b3115433ea8508a82d6050d48 10_3390_rs11111293
GeographicLocations	Beijing China New York United States--US China
GeographicLocations_xml	– name: New York – name: China – name: Beijing China – name: United States--US
GroupedDBID	29P 2WC 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI
ID	FETCH-LOGICAL-c427t-24cdab9ad482309d57fecc1166ae2d9632b13c4b57f859dad1585d3e091cbc3c3
IEDL.DBID	DOA
ISICitedReferencesCount	51
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000472648000036&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2072-4292
IngestDate	Mon Nov 10 04:33:36 EST 2025 Fri Jul 25 12:14:49 EDT 2025 Tue Nov 18 21:18:38 EST 2025 Sat Nov 29 07:16:44 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c427t-24cdab9ad482309d57fecc1166ae2d9632b13c4b57f859dad1585d3e091cbc3c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-6551-6807 0000-0001-6827-4225 0000-0002-6916-4332
OpenAccessLink	https://doaj.org/article/77bdde4b3115433ea8508a82d6050d48
PQID	2304043489
PQPubID	2032338
ParticipantIDs	doaj_primary_oai_doaj_org_article_77bdde4b3115433ea8508a82d6050d48 proquest_journals_2304043489 crossref_primary_10_3390_rs11111293 crossref_citationtrail_10_3390_rs11111293
PublicationCentury	2000
PublicationDate	2019-06-01
PublicationDateYYYYMMDD	2019-06-01
PublicationDate_xml	– month: 06 year: 2019 text: 2019-06-01 day: 01
PublicationDecade	2010
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Remote sensing (Basel, Switzerland)
PublicationYear	2019
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	ref_50 ref_14 ref_11 ref_10 ref_54 ref_52 ref_51 Zhang (ref_12) 2017; 5 ref_17 ref_15 He (ref_13) 2016; 18 ref_25 ref_24 ref_23 Friedman (ref_53) 2001; 29 ref_22 ref_21 ref_20 ref_29 Sun (ref_42) 2018; 67 ref_27 ref_26 ref_36 ref_35 ref_34 ref_33 Shao (ref_4) 2016; 2016 ref_32 ref_31 Vincent (ref_43) 2010; 11 ref_39 ref_38 Khatab (ref_30) 2017; 2 Rizk (ref_28) 2018; 19 Shao (ref_37) 2018; 6 Ficco (ref_18) 2014; 13 ref_47 ref_46 ref_44 ref_41 Chai (ref_19) 2007; 6 ref_40 ref_1 ref_3 Cybenko (ref_45) 1989; 2 ref_2 Xiao (ref_16) 2017; 5 ref_49 ref_48 ref_9 ref_8 ref_5 ref_7 ref_6
References_xml	– ident: ref_26 doi: 10.1109/WIMOB.2007.4390815 – ident: ref_46 doi: 10.1145/1390156.1390294 – ident: ref_33 doi: 10.1109/CANDARW.2018.00045 – ident: ref_49 – volume: 2016 start-page: 1 year: 2016 ident: ref_4 article-title: Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning publication-title: J. Sens. doi: 10.1155/2016/1945695 – ident: ref_51 – volume: 6 start-page: 74699 year: 2018 ident: ref_37 article-title: Indoor Positioning Based on Fingerprint-Image and Deep Learning publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2884193 – volume: 19 start-page: 2305 year: 2018 ident: ref_28 article-title: CellinDeep: Robust and Accurate Cellular-based Indoor Localization via Deep Learning publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2018.2885958 – ident: ref_5 doi: 10.3390/rs11030294 – ident: ref_1 doi: 10.1177/1550147718815637 – ident: ref_39 doi: 10.1109/VTCFall.2018.8690989 – volume: 13 start-page: 737 year: 2014 ident: ref_18 article-title: Calibrating Indoor Positioning Systemswith Low Efforts[M] publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2013.29 – ident: ref_35 – volume: 5 start-page: 13756 year: 2017 ident: ref_12 article-title: Path-loss-based fingerprint localization approach for location-based services in indoor environments publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2728789 – volume: 29 start-page: 1189 year: 2001 ident: ref_53 article-title: Greedy Function Approximation: A Gradient Boosting Machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – volume: 6 start-page: 649 year: 2007 ident: ref_19 article-title: Reducing the calibration effort for probabilistic indoor location estimation publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2007.1025 – ident: ref_27 – ident: ref_3 doi: 10.3390/s17112678 – ident: ref_48 – ident: ref_29 doi: 10.1109/ICMLA.2017.0-185 – ident: ref_24 doi: 10.1109/ISDFS.2018.8355353 – ident: ref_2 doi: 10.1109/IPIN.2016.7743700 – ident: ref_52 doi: 10.1145/2939672.2939785 – ident: ref_34 doi: 10.1007/978-3-319-54042-9_57 – ident: ref_17 doi: 10.1109/IPIN.2016.7743678 – ident: ref_31 doi: 10.1109/CCDC.2016.7532172 – volume: 18 start-page: 466 year: 2016 ident: ref_13 article-title: Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2015.2464084 – ident: ref_25 doi: 10.3390/rs11060652 – ident: ref_23 doi: 10.1109/SMC.2014.6974105 – ident: ref_11 doi: 10.1145/1859995.1860016 – ident: ref_15 doi: 10.1109/IPIN.2017.8115943 – ident: ref_47 – ident: ref_54 doi: 10.1201/b12207 – ident: ref_6 doi: 10.3390/rs11050566 – ident: ref_38 doi: 10.1109/ICC.2018.8422562 – ident: ref_20 doi: 10.1109/ComComAp.2014.7017163 – ident: ref_40 – ident: ref_14 – ident: ref_41 doi: 10.1109/WoWMoM.2017.7974313 – ident: ref_44 – ident: ref_36 doi: 10.1109/ICC.2017.7997235 – ident: ref_21 – ident: ref_32 doi: 10.1109/FOAN.2017.8215259 – ident: ref_8 doi: 10.3390/s18103317 – volume: 2 start-page: 1 year: 2017 ident: ref_30 article-title: A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine publication-title: IEEE Sens. Lett. doi: 10.1109/LSENS.2017.2787651 – ident: ref_50 – volume: 11 start-page: 3371 year: 2010 ident: ref_43 article-title: Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion publication-title: J. Mach. Learn. Res. – ident: ref_9 doi: 10.1145/2462456.2464463 – volume: 2 start-page: 303 year: 1989 ident: ref_45 article-title: Approximation by superpositions of a sigmoidal function publication-title: Mathematics of Control, Signals and Systems doi: 10.1007/BF02551274 – volume: 67 start-page: 10896 year: 2018 ident: ref_42 article-title: Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2870160 – ident: ref_22 – ident: ref_7 doi: 10.1177/1550147718758263 – ident: ref_10 doi: 10.1109/WCNC.2007.392 – volume: 5 start-page: 12751 year: 2017 ident: ref_16 article-title: 3-D BLE Indoor Localization Based on Denoising Autoencoder publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2720164
SSID	ssj0000331904
Score	2.422964
Snippet	With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its...
SourceID	doaj proquest crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	1293
SubjectTerms	Accuracy Algorithms Artificial intelligence Communications networks Data collection Datasets Deep learning Feature extraction Fingerprints Indoor positioning International conferences Localization Location based services Machine learning multi-layer perceptron Multilayer perceptrons Neural networks Noise reduction Position (location) Regression analysis Regression models regression positioning Remote sensing Robustness stacked denoising autoencoder Teaching methods Training Wireless networks
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3PT9swFH5iZdJ2GYwNrRsgS-PCwSKJ3SY-TYVRcUBVhUBwixzbgUqQQJJO2n-_9xy3aNq0y64vlpXo8_vpl-8BHOpxYYS2ko_NKOPojw3PSp1xF4kiVU4Tx5QfNpHOZtntrZqHglsb2ipXNtEbalsbqpEfU_EykkJm6tvTM6epUXS7GkZovIJNYiqTA9g8OZvNL9dVlkjgEYtkz0sqML8_bloyEuTlfvNEnrD_D3vsncx0639fbxvehfCSTfrz8B42XLUDb8Kk8_ufH6CesMu6WLYdu1nw6YJNfVmPqnsdm4cGLhSxycMdbt_dPzLfVMAwKEV9t-y7q-qFl0yWXU0smNY1TFeW-V95-YXGGJ7N-3aZpq4-wvX07Or0nIehC9zIJO14Io3VhUL46ApO2VFaIspxPB5rl1hU16SIhZEFyrORstrGmHBY4TDuMAi7EbswqOrKfQKmiXsMdzOJsFKYRLu4LGOlVaqsELIcwtEKgNwERnIajPGQY2ZCYOUvYA3h63rtU8_D8ddVJ4TjegVxZ3tB3dzlQRXzNC3QpsuCeIakEE5nGKTqLLGY2UX42UPYW0GcB4Vu8xd8P__78Rd4izGV6rvJ9mDQNUu3D6_Nj27RNgfhfP4CmRTxNQ priority: 102 providerName: ProQuest
Title	A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron
URI	https://www.proquest.com/docview/2304043489 https://doaj.org/article/77bdde4b3115433ea8508a82d6050d48
Volume	11
WOSCitedRecordID	wos000472648000036&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NS8MwFA8yBb2InzidI6AXD8G1ydbmuOmGgo4yFT8uJU1SN5itdJ3gxb_dl7SbEwUvXnp4DW15H8nvpS-_h9CxaEWSCsVISzZ9AuuxJH4sfKIbNPK4FoZjyjab8Pp9_-GBBwutvkxNWEEPXCju1PMiiEAWGVYYRqkWPkAK4bsKcHhDMXvMF1DPQjJl52AKrtVgBR8phbz-NJuYycGsbt9WIEvU_2MetotLbwOtl6gQt4uv2URLOtlCq2WD8uH7NkrbeJBG00mO70ekN8I9uxtnNuVyHJR1VyDC7fFzCun-8AXbWgAMWBLCVOFznaQjK2lP89SQVyqdYZEobE_gkisB0BsHRZVLliY76K7XvT27IGWvBCKZ6-XEZVKJiIPWzZ8zrppeDMZxnFZLaFdBlLmRQyWLQO43uRLKgTxBUQ1wQYK1JN1FlSRN9B7CwlCGwdOkSxWj0hXaiWOHC-5xRSmLq-hkpr9QlkTipp_FOISEwug6_NJ1FR3Nx74W9Bm_juoYM8xHGMprKwBHCEtHCP9yhCqqzYwYlnE4Cc2Wd4NR5vP9_3jHAVoDwMSLUrEaquTZVB-iFfmWjyZZHS13uv1gULeuWDdVpDfm-tGFa9B8gvvB5XXw-Amh6eed
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLVK58EYsFLAEHDhYTWzvJj4gtFBWXXW7ilAR5RQc22lXapM2yYL6p_iNjJ1kKwTi1gNXx7Iy8Zd5efwNwCs1zjRXRtCxHsUU7bGmca5iagOeRdIqxzHlm01Ei0V8dCSTDfjZ34VxZZW9TvSK2pTa5ch3XPIyEFzE8t35BXVdo9zpat9Co4XFvr38gSFb_Xa2i_v7mrHpx8MPe7TrKkC1YFFDmdBGZRLfz50xSTOKchQjDMdjZZlBPLIs5FpkOB6PpFEmRI_acIuGVaNcmuO6N2BTINjjAWwms4Pk6zqrE3CEdCBaHlTOZbBT1U4pOav6m-XzDQL-0P_eqE3v_G-f4y7c7txnMmnxfg82bHEftrpO7ieXD6CckE9ltqob8mVJp0sy9WlLl71sSNIVqOEQmZweozjNyRnxRRMEnW7UZ4bs2qJc-pHJqikdy6exFVGFIf6qMp0rjFFI0pYDVWXxED5fi7yPYFCUhX0MRDluNVxNM24E10zZMM9DqWQkDeciH8KbfsNT3TGuu8YfpylGXg4c6RU4hvByPfe85Rn566z3DjfrGY4b3A-U1XHaqZo0ijK0WSJzPEqCc6tidMJVzAxGrgGKPYTtHlJpp7Dq9ApPT_79-AVs7R0ezNP5bLH_FG6h_yjbyrltGDTVyj6Dm_p7s6yr592_QeDbdePvFz7MT7E
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLQIuvBELBSwBBw7WJrY3iQ8IpV0iqlarqALRW3Bsp12pJCXJgvrX-HWM89gKgbj1wNWxrEz8ZV4efwPwSgW55soIGuh5RNEeaxoVKqLW43korXIcU12ziXC5jI6PZboFP8e7MK6sctSJnaI2lXY58plLXnqCi0jOiqEsIl0k786_UddByp20ju00eogc2IsfGL41b_cXuNevGUvef9z7QIcOA1QLFraUCW1ULvFd3XmTNPOwQJF8PwiUZQaxyXKfa5HjeDSXRhkfvWvDLRpZjTJqjuteg-0oCD02ge10bzc-2mR4PI7w9kTPicq59GZ14xSUs7C_WcGuWcAftqAzcMmd__nT3IXbg1tN4v4_uAdbtrwPN4cO76cXD6CKyVGVr5uWfF7RZEWSLp3pspotSYfCNRwi8dkJitOefiVdMQVBZxz1nCELW1arbiRet5Vj_zS2Jqo0pLvCTA8Vxi4k7cuE6qp8CJ-uRN5HMCmr0j4GohznGq6mGTeCa6asXxS-VDKUhnNRTOHNuPmZHpjYXUOQswwjMgeU7BIoU3i5mXve84_8ddauw9BmhuMM7waq-iQbVFAWhjnaMpE7fiXBuVUROucqYgYjWg_FnsLOCK9sUGRNdomtJ_9-_AJuIOiyw_3lwVO4hW6l7AvqdmDS1mv7DK7r7-2qqZ8PvwmBL1cNv18TIVgh
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Robust+Wi-Fi+Fingerprint+Positioning+Algorithm+Using+Stacked+Denoising+Autoencoder+and+Multi-Layer+Perceptron&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Rongrong+Wang&rft.au=Zhaohui+Li&rft.au=Haiyong+Luo&rft.au=Fang+Zhao&rft.date=2019-06-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=11&rft.issue=11&rft.spage=1293&rft_id=info:doi/10.3390%2Frs11111293&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_77bdde4b3115433ea8508a82d6050d48
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon