Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi
A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to addr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/9/3204 |
| _version_ | 1797502949724782592 |
|---|---|
| author | Ghazaleh Kia Laura Ruotsalainen Jukka Talvitie |
| author_facet | Ghazaleh Kia Laura Ruotsalainen Jukka Talvitie |
| author_sort | Ghazaleh Kia |
| collection | DOAJ |
| description | A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution. |
| first_indexed | 2024-03-10T03:43:29Z |
| format | Article |
| id | doaj.art-e97f57b1ac9a461f9a10d827a9559a64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:43:29Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e97f57b1ac9a461f9a10d827a9559a642023-11-23T09:14:57ZengMDPI AGSensors1424-82202022-04-01229320410.3390/s22093204Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFiGhazaleh Kia0Laura Ruotsalainen1Jukka Talvitie2Department of Computer Science, University of Helsinki, 00014 Helsinki, FinlandDepartment of Computer Science, University of Helsinki, 00014 Helsinki, FinlandUnit of Electrical Engineering, Tampere University, 33014 Tampere, FinlandA wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution.https://www.mdpi.com/1424-8220/22/9/3204fusionGaussian processindoor position estimationmachine learningRSSRTT |
| spellingShingle | Ghazaleh Kia Laura Ruotsalainen Jukka Talvitie Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi Sensors fusion Gaussian process indoor position estimation machine learning RSS RTT |
| title | Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi |
| title_full | Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi |
| title_fullStr | Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi |
| title_full_unstemmed | Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi |
| title_short | Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi |
| title_sort | toward accurate indoor positioning an rss based fusion of uwb and machine learning enhanced wifi |
| topic | fusion Gaussian process indoor position estimation machine learning RSS RTT |
| url | https://www.mdpi.com/1424-8220/22/9/3204 |
| work_keys_str_mv | AT ghazalehkia towardaccurateindoorpositioninganrssbasedfusionofuwbandmachinelearningenhancedwifi AT lauraruotsalainen towardaccurateindoorpositioninganrssbasedfusionofuwbandmachinelearningenhancedwifi AT jukkatalvitie towardaccurateindoorpositioninganrssbasedfusionofuwbandmachinelearningenhancedwifi |