SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate
The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/823 |
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| author | Rahul Mundlamuri Devasena Inupakutika David Akopian |
| author_facet | Rahul Mundlamuri Devasena Inupakutika David Akopian |
| author_sort | Rahul Mundlamuri |
| collection | DOAJ |
| description | The Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In particular, received signal strength indicators (RSSIs) and Channel State Information (CSI) in Wireless Local Area Networks (WLANs or Wi-Fi) have gained popularity and have been addressed in the literature. While RSSI signatures are easy to collect, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multi-floor or even three-dimensional (3D) indoor localization. Considering recent E911 mandate attention to vertical location accuracy, this study aimed to investigate CSI from Wi-Fi signals to produce baseline Z-axis location data, which has not been thoroughly addressed. To that end, we utilized CSI measurements and two representative machine learning methods, an artificial neural network (ANN) and convolutional neural network (CNN), to estimate both 3D and vertical-axis positioning feasibility to achieve E911 accuracy compliance. |
| format | Article |
| id | doaj-art-22c7d01182a040509adfc3de800615bb |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-22c7d01182a040509adfc3de800615bb2025-08-20T03:12:35ZengMDPI AGSensors1424-82202025-01-0125382310.3390/s25030823SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy MandateRahul Mundlamuri0Devasena Inupakutika1David Akopian2Electrical and Computer Engineering Department, The University of Texas at San Antonio, San Antonio, TX 78249-0670, USAElectrical and Computer Engineering Department, The University of Texas at San Antonio, San Antonio, TX 78249-0670, USAElectrical and Computer Engineering Department, The University of Texas at San Antonio, San Antonio, TX 78249-0670, USAThe Enhanced 911 (E911) mandate of the Federal Communications Commission (FCC) drives the evolution of indoor three-dimensional (3D) location/positioning services for emergency calls. Many indoor localization systems exploit location-dependent wireless signaling signatures, often called fingerprints, and machine learning techniques for position estimation. In particular, received signal strength indicators (RSSIs) and Channel State Information (CSI) in Wireless Local Area Networks (WLANs or Wi-Fi) have gained popularity and have been addressed in the literature. While RSSI signatures are easy to collect, the fluctuation of wireless signals resulting from environmental uncertainties leads to considerable variations in RSSIs, which poses a challenge to accurate localization on a single floor, not to mention multi-floor or even three-dimensional (3D) indoor localization. Considering recent E911 mandate attention to vertical location accuracy, this study aimed to investigate CSI from Wi-Fi signals to produce baseline Z-axis location data, which has not been thoroughly addressed. To that end, we utilized CSI measurements and two representative machine learning methods, an artificial neural network (ANN) and convolutional neural network (CNN), to estimate both 3D and vertical-axis positioning feasibility to achieve E911 accuracy compliance.https://www.mdpi.com/1424-8220/25/3/823E9113D indoor localizationchannel state informationfloor detectionneural networks |
| spellingShingle | Rahul Mundlamuri Devasena Inupakutika David Akopian SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate Sensors E911 3D indoor localization channel state information floor detection neural networks |
| title | SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate |
| title_full | SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate |
| title_fullStr | SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate |
| title_full_unstemmed | SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate |
| title_short | SDR-Fi-Z: A Wireless Local Area Network-Fingerprinting-Based Indoor Positioning Method for E911 Vertical Accuracy Mandate |
| title_sort | sdr fi z a wireless local area network fingerprinting based indoor positioning method for e911 vertical accuracy mandate |
| topic | E911 3D indoor localization channel state information floor detection neural networks |
| url | https://www.mdpi.com/1424-8220/25/3/823 |
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