Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach
Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environme...
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MDPI AG
2024-09-01
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| author | Chengjie Hou Zhizhong Zhang |
| author_facet | Chengjie Hou Zhizhong Zhang |
| author_sort | Chengjie Hou |
| collection | DOAJ |
| description | Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms. |
| format | Article |
| id | doaj-art-68e29ea91ba64d2c90682ba7b81799e8 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-68e29ea91ba64d2c90682ba7b81799e82025-08-20T01:55:50ZengMDPI AGSensors1424-82202024-09-012418594010.3390/s24185940Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian ApproachChengjie Hou0Zhizhong Zhang1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400000, ChinaSchool of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210000, ChinaFingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.https://www.mdpi.com/1424-8220/24/18/5940indoor localizationmulti-fingerprintsnaive BayesianWi-Fi fingerprints |
| spellingShingle | Chengjie Hou Zhizhong Zhang Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach Sensors indoor localization multi-fingerprints naive Bayesian Wi-Fi fingerprints |
| title | Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach |
| title_full | Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach |
| title_fullStr | Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach |
| title_full_unstemmed | Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach |
| title_short | Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach |
| title_sort | multi fingerprints indoor localization for variable spatial environments a naive bayesian approach |
| topic | indoor localization multi-fingerprints naive Bayesian Wi-Fi fingerprints |
| url | https://www.mdpi.com/1424-8220/24/18/5940 |
| work_keys_str_mv | AT chengjiehou multifingerprintsindoorlocalizationforvariablespatialenvironmentsanaivebayesianapproach AT zhizhongzhang multifingerprintsindoorlocalizationforvariablespatialenvironmentsanaivebayesianapproach |