Trusted K Nearest Bayesian Estimation for Indoor Positioning System
Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inerti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8695741/ |
| _version_ | 1818924130286698496 |
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| author | Rohan Kumar Yadav Bimal Bhattarai Hui-Seon Gang Jae-Young Pyun |
| author_facet | Rohan Kumar Yadav Bimal Bhattarai Hui-Seon Gang Jae-Young Pyun |
| author_sort | Rohan Kumar Yadav |
| collection | DOAJ |
| description | Indoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ultra-wideband. The major problem with Bluetooth-based fingerprinting is the inconsistency of the radio signal strength, and the IMU-based localization has a drift error that increases with time. To compensate for these drawbacks, in the present study, a novel positioning system with IMU sensors and Bluetooth low energy (BLE) beacon for a smartphone are introduced. The proposed trusted K nearest Bayesian estimation (TKBE) integrates BLE beacon and pedestrian dead reckoning positionings. The BLE-based positioning, using both the K-nearest neighbor (KNN) and Bayesian estimation, increases the accuracy by 25% compared with the existing KNN-based positioning, and the proposed fuzzy logic-based Kalman filter increases the accuracy by an additional 15%. The overall performance of TKBE has an error of <;1 m in our experimental environments. |
| first_indexed | 2024-12-20T02:20:26Z |
| format | Article |
| id | doaj.art-106f725c61b34bd29d7dff8aeb21e590 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:20:26Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-106f725c61b34bd29d7dff8aeb21e5902022-12-21T19:56:50ZengIEEEIEEE Access2169-35362019-01-017514845149810.1109/ACCESS.2019.29103148695741Trusted K Nearest Bayesian Estimation for Indoor Positioning SystemRohan Kumar Yadav0https://orcid.org/0000-0003-1485-0439Bimal Bhattarai1https://orcid.org/0000-0002-7339-3621Hui-Seon Gang2Jae-Young Pyun3Department of Information Communication Engineering, Chosun University, Gwangju, South KoreaDepartment of Information Communication Engineering, Chosun University, Gwangju, South KoreaDepartment of Information Communication Engineering, Chosun University, Gwangju, South KoreaDepartment of Information Communication Engineering, Chosun University, Gwangju, South KoreaIndoor positioning systems have received increasing attention because of their wide range of indoor applications. However, the positioning system generally suffers from a large error in localization and has low solidity. The main approaches widely used for indoor localization are based on the inertial measurement unit (IMU), Bluetooth, Wi-Fi, and ultra-wideband. The major problem with Bluetooth-based fingerprinting is the inconsistency of the radio signal strength, and the IMU-based localization has a drift error that increases with time. To compensate for these drawbacks, in the present study, a novel positioning system with IMU sensors and Bluetooth low energy (BLE) beacon for a smartphone are introduced. The proposed trusted K nearest Bayesian estimation (TKBE) integrates BLE beacon and pedestrian dead reckoning positionings. The BLE-based positioning, using both the K-nearest neighbor (KNN) and Bayesian estimation, increases the accuracy by 25% compared with the existing KNN-based positioning, and the proposed fuzzy logic-based Kalman filter increases the accuracy by an additional 15%. The overall performance of TKBE has an error of <;1 m in our experimental environments.https://ieeexplore.ieee.org/document/8695741/Bayesian estimationBluetooth low energy (BLE)fingerprintsfuzzy-logic systemindoor positioningK-nearest neighbor (KNN) |
| spellingShingle | Rohan Kumar Yadav Bimal Bhattarai Hui-Seon Gang Jae-Young Pyun Trusted K Nearest Bayesian Estimation for Indoor Positioning System IEEE Access Bayesian estimation Bluetooth low energy (BLE) fingerprints fuzzy-logic system indoor positioning K-nearest neighbor (KNN) |
| title | Trusted K Nearest Bayesian Estimation for Indoor Positioning System |
| title_full | Trusted K Nearest Bayesian Estimation for Indoor Positioning System |
| title_fullStr | Trusted K Nearest Bayesian Estimation for Indoor Positioning System |
| title_full_unstemmed | Trusted K Nearest Bayesian Estimation for Indoor Positioning System |
| title_short | Trusted K Nearest Bayesian Estimation for Indoor Positioning System |
| title_sort | trusted k nearest bayesian estimation for indoor positioning system |
| topic | Bayesian estimation Bluetooth low energy (BLE) fingerprints fuzzy-logic system indoor positioning K-nearest neighbor (KNN) |
| url | https://ieeexplore.ieee.org/document/8695741/ |
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