Indoor Wi-Fi Positioning Algorithm Based on Location Fingerprint
Currently most of the existing indoor fingerprint positioning algorithms are based on fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality...
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| Veröffentlicht in: | Mobile networks and applications 2021-02, Vol.26 (1), p.146-155 |
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| creator | Cui, Xuerong Wang, Mengyan Li, Juan Ji, Meiqi Yang, Jin Liu, Jianhang Huang, Tingpei Chen, Haihua |
| description | Currently most of the existing indoor fingerprint positioning algorithms are based on fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality test and Kalman filter fusion is proposed. In the training phase, the RSSI (Received Signal Strength Indication) samples received on each fingerprint point are tested based on the skewness-kurtosis normality. If the normal distribution model is met, the normal distribution function is used to estimate the probability density of the samples. If not the kernel function will be used. And then the value of the large probability density is taken for Kalman filtering, and finally, the averaged value after filtering is used to establish a high-precision fingerprint database. In the online positioning stage, the weighted KNN (K-Nearest Neighbor) is used to estimate the position, and finally, the positioning point is corrected by the fusion of the Levenberg-Marquardt method and the Kalman filter. The optimization of the three stages can improve the positioning accuracy. The simulation results show that the indoor positioning method proposed in this paper has the least number of iterations and the positioning accuracy is improved by 60% compared with the traditional Kalman filtering method. |
| doi_str_mv | 10.1007/s11036-020-01686-1 |
| format | Article |
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The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality test and Kalman filter fusion is proposed. In the training phase, the RSSI (Received Signal Strength Indication) samples received on each fingerprint point are tested based on the skewness-kurtosis normality. If the normal distribution model is met, the normal distribution function is used to estimate the probability density of the samples. If not the kernel function will be used. And then the value of the large probability density is taken for Kalman filtering, and finally, the averaged value after filtering is used to establish a high-precision fingerprint database. In the online positioning stage, the weighted KNN (K-Nearest Neighbor) is used to estimate the position, and finally, the positioning point is corrected by the fusion of the Levenberg-Marquardt method and the Kalman filter. The optimization of the three stages can improve the positioning accuracy. The simulation results show that the indoor positioning method proposed in this paper has the least number of iterations and the positioning accuracy is improved by 60% compared with the traditional Kalman filtering method.</description><identifier>ISSN: 1383-469X</identifier><identifier>EISSN: 1572-8153</identifier><identifier>DOI: 10.1007/s11036-020-01686-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Algorithms ; Communications Engineering ; Computer Communication Networks ; Density ; Distribution functions ; Electrical Engineering ; Engineering ; Fingerprinting ; Fingerprints ; IT in Business ; Kalman filters ; Kernel functions ; Kurtosis ; Networks ; Normal distribution ; Normality ; Optimization ; Signal strength ; Skewness ; Statistical analysis</subject><ispartof>Mobile networks and applications, 2021-02, Vol.26 (1), p.146-155</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2021</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-eee55f86c171cbfe2e0a7762bcd9f7e86771e546d42b20643c2f50a95464c9d33</citedby><cites>FETCH-LOGICAL-c319t-eee55f86c171cbfe2e0a7762bcd9f7e86771e546d42b20643c2f50a95464c9d33</cites><orcidid>0000-0002-2326-9518</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11036-020-01686-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11036-020-01686-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27915,27916,41479,42548,51310</link.rule.ids></links><search><creatorcontrib>Cui, Xuerong</creatorcontrib><creatorcontrib>Wang, Mengyan</creatorcontrib><creatorcontrib>Li, Juan</creatorcontrib><creatorcontrib>Ji, Meiqi</creatorcontrib><creatorcontrib>Yang, Jin</creatorcontrib><creatorcontrib>Liu, Jianhang</creatorcontrib><creatorcontrib>Huang, Tingpei</creatorcontrib><creatorcontrib>Chen, Haihua</creatorcontrib><title>Indoor Wi-Fi Positioning Algorithm Based on Location Fingerprint</title><title>Mobile networks and applications</title><addtitle>Mobile Netw Appl</addtitle><description>Currently most of the existing indoor fingerprint positioning algorithms are based on fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality test and Kalman filter fusion is proposed. In the training phase, the RSSI (Received Signal Strength Indication) samples received on each fingerprint point are tested based on the skewness-kurtosis normality. If the normal distribution model is met, the normal distribution function is used to estimate the probability density of the samples. If not the kernel function will be used. And then the value of the large probability density is taken for Kalman filtering, and finally, the averaged value after filtering is used to establish a high-precision fingerprint database. In the online positioning stage, the weighted KNN (K-Nearest Neighbor) is used to estimate the position, and finally, the positioning point is corrected by the fusion of the Levenberg-Marquardt method and the Kalman filter. The optimization of the three stages can improve the positioning accuracy. The simulation results show that the indoor positioning method proposed in this paper has the least number of iterations and the positioning accuracy is improved by 60% compared with the traditional Kalman filtering method.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Communications Engineering</subject><subject>Computer Communication Networks</subject><subject>Density</subject><subject>Distribution functions</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Fingerprinting</subject><subject>Fingerprints</subject><subject>IT in Business</subject><subject>Kalman filters</subject><subject>Kernel functions</subject><subject>Kurtosis</subject><subject>Networks</subject><subject>Normal distribution</subject><subject>Normality</subject><subject>Optimization</subject><subject>Signal strength</subject><subject>Skewness</subject><subject>Statistical analysis</subject><issn>1383-469X</issn><issn>1572-8153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9UE1LAzEQDaJgrf4BTwHP0cn35mYtVgsFPSh6C9tstqa0m5psD_57U1fw5mmGmffezHsIXVK4pgD6JlMKXBFgQICqShF6hEZUakYqKvlx6XnFiVDm_RSd5bwGACkrMUK3866JMeG3QGYBP8cc-hC70K3wZLOKKfQfW3xXZ9_g2OFFdPVhjWcF4NMuha4_Rydtvcn-4reO0evs_mX6SBZPD_PpZEEcp6Yn3nsp20o5qqlbtp55qLVWbOka02pfKa2pl0I1gi0ZKMEdayXUpoyEMw3nY3Q16O5S_Nz73Nt13KeunLRMgtTGCAkFxQaUSzHn5FtbntzW6ctSsIek7JCULUnZn6QsLSQ-kPLBUTH2J_0P6xuJNWqj</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Cui, Xuerong</creator><creator>Wang, Mengyan</creator><creator>Li, Juan</creator><creator>Ji, Meiqi</creator><creator>Yang, Jin</creator><creator>Liu, Jianhang</creator><creator>Huang, Tingpei</creator><creator>Chen, Haihua</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-2326-9518</orcidid></search><sort><creationdate>20210201</creationdate><title>Indoor Wi-Fi Positioning Algorithm Based on Location Fingerprint</title><author>Cui, Xuerong ; Wang, Mengyan ; Li, Juan ; Ji, Meiqi ; Yang, Jin ; Liu, Jianhang ; Huang, Tingpei ; Chen, Haihua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-eee55f86c171cbfe2e0a7762bcd9f7e86771e546d42b20643c2f50a95464c9d33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Communications Engineering</topic><topic>Computer Communication Networks</topic><topic>Density</topic><topic>Distribution functions</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Fingerprinting</topic><topic>Fingerprints</topic><topic>IT in Business</topic><topic>Kalman filters</topic><topic>Kernel functions</topic><topic>Kurtosis</topic><topic>Networks</topic><topic>Normal distribution</topic><topic>Normality</topic><topic>Optimization</topic><topic>Signal strength</topic><topic>Skewness</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cui, Xuerong</creatorcontrib><creatorcontrib>Wang, Mengyan</creatorcontrib><creatorcontrib>Li, Juan</creatorcontrib><creatorcontrib>Ji, Meiqi</creatorcontrib><creatorcontrib>Yang, Jin</creatorcontrib><creatorcontrib>Liu, Jianhang</creatorcontrib><creatorcontrib>Huang, Tingpei</creatorcontrib><creatorcontrib>Chen, Haihua</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ABI/INFORM Complete</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Mobile networks and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cui, Xuerong</au><au>Wang, Mengyan</au><au>Li, Juan</au><au>Ji, Meiqi</au><au>Yang, Jin</au><au>Liu, Jianhang</au><au>Huang, Tingpei</au><au>Chen, Haihua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Indoor Wi-Fi Positioning Algorithm Based on Location Fingerprint</atitle><jtitle>Mobile networks and applications</jtitle><stitle>Mobile Netw Appl</stitle><date>2021-02-01</date><risdate>2021</risdate><volume>26</volume><issue>1</issue><spage>146</spage><epage>155</epage><pages>146-155</pages><issn>1383-469X</issn><eissn>1572-8153</eissn><abstract>Currently most of the existing indoor fingerprint positioning algorithms are based on fingerprint database. The accuracy of the fingerprint database will directly affect the final positioning accuracy. Therefore, through the research of fingerprint data, a method based on skewness-kurtosis normality test and Kalman filter fusion is proposed. In the training phase, the RSSI (Received Signal Strength Indication) samples received on each fingerprint point are tested based on the skewness-kurtosis normality. If the normal distribution model is met, the normal distribution function is used to estimate the probability density of the samples. If not the kernel function will be used. And then the value of the large probability density is taken for Kalman filtering, and finally, the averaged value after filtering is used to establish a high-precision fingerprint database. In the online positioning stage, the weighted KNN (K-Nearest Neighbor) is used to estimate the position, and finally, the positioning point is corrected by the fusion of the Levenberg-Marquardt method and the Kalman filter. The optimization of the three stages can improve the positioning accuracy. The simulation results show that the indoor positioning method proposed in this paper has the least number of iterations and the positioning accuracy is improved by 60% compared with the traditional Kalman filtering method.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11036-020-01686-1</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-2326-9518</orcidid></addata></record> |
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| subjects | Accuracy Algorithms Communications Engineering Computer Communication Networks Density Distribution functions Electrical Engineering Engineering Fingerprinting Fingerprints IT in Business Kalman filters Kernel functions Kurtosis Networks Normal distribution Normality Optimization Signal strength Skewness Statistical analysis |
| title | Indoor Wi-Fi Positioning Algorithm Based on Location Fingerprint |
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