Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning
Fingerprinting techniques are known to perform better for radio-frequency-based indoor positioning compared to lateration-based techniques. However, accurate fingerprinting depends on a thorough prior scene analysis, in which the area should be described in terms of the signal parameters the positio...
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| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Indoor and Seamless Positioning and Navigation |
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| Online Access: | https://ieeexplore.ieee.org/document/10349912/ |
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| author | F. Serhan Danis |
| author_facet | F. Serhan Danis |
| author_sort | F. Serhan Danis |
| collection | DOAJ |
| description | Fingerprinting techniques are known to perform better for radio-frequency-based indoor positioning compared to lateration-based techniques. However, accurate fingerprinting depends on a thorough prior scene analysis, in which the area should be described in terms of the signal parameters the positioning system deploys. This requires a heavy workload to build accurate systems, causing a tradeoff between accuracy and practicality. In this article, we propose a chain of subsequent preprocessing techniques for generating accurate radio frequency maps (RMs). The techniques consist of filtering the received signal strength indicator and interpolating the local probability distribution parameters. The proposed subsequent techniques generate smoother RMs and describe these maps with only two parameters per position. By plugging an adaptive particle filter as the position estimation algorithm, we show that the generated RMs increase the positioning accuracy significantly. We also investigate the relation between practicality and accuracy in terms of the invested time in the process of fingerprinting and the stored data to represent the RM. Alongside the increased accuracy of the proposed system, the approach allows a dramatic increase in the practicality of the fingerprinting technique. |
| format | Article |
| id | doaj-art-cc304d4fb9fb4b9da1d5f41ee4d334df |
| institution | DOAJ |
| issn | 2832-7322 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Indoor and Seamless Positioning and Navigation |
| spelling | doaj-art-cc304d4fb9fb4b9da1d5f41ee4d334df2025-08-20T02:53:07ZengIEEEIEEE Journal of Indoor and Seamless Positioning and Navigation2832-73222023-01-01119921010.1109/JISPIN.2023.334063810349912Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor PositioningF. Serhan Danis0https://orcid.org/0000-0002-8813-9220GEOLOC Laboratory, Gustave Eiffel University, Bouguenais, FranceFingerprinting techniques are known to perform better for radio-frequency-based indoor positioning compared to lateration-based techniques. However, accurate fingerprinting depends on a thorough prior scene analysis, in which the area should be described in terms of the signal parameters the positioning system deploys. This requires a heavy workload to build accurate systems, causing a tradeoff between accuracy and practicality. In this article, we propose a chain of subsequent preprocessing techniques for generating accurate radio frequency maps (RMs). The techniques consist of filtering the received signal strength indicator and interpolating the local probability distribution parameters. The proposed subsequent techniques generate smoother RMs and describe these maps with only two parameters per position. By plugging an adaptive particle filter as the position estimation algorithm, we show that the generated RMs increase the positioning accuracy significantly. We also investigate the relation between practicality and accuracy in terms of the invested time in the process of fingerprinting and the stored data to represent the RM. Alongside the increased accuracy of the proposed system, the approach allows a dramatic increase in the practicality of the fingerprinting technique.https://ieeexplore.ieee.org/document/10349912/Convex combinationfingerprintingindoor positioning systemsparameterizationradio frequency map smoothingreal-time data processing |
| spellingShingle | F. Serhan Danis Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning IEEE Journal of Indoor and Seamless Positioning and Navigation Convex combination fingerprinting indoor positioning systems parameterization radio frequency map smoothing real-time data processing |
| title | Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning |
| title_full | Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning |
| title_fullStr | Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning |
| title_full_unstemmed | Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning |
| title_short | Practical and Parameterized Fingerprinting Through Maximal Filtering for Indoor Positioning |
| title_sort | practical and parameterized fingerprinting through maximal filtering for indoor positioning |
| topic | Convex combination fingerprinting indoor positioning systems parameterization radio frequency map smoothing real-time data processing |
| url | https://ieeexplore.ieee.org/document/10349912/ |
| work_keys_str_mv | AT fserhandanis practicalandparameterizedfingerprintingthroughmaximalfilteringforindoorpositioning |