A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI
Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath envi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6404 |
| _version_ | 1827665056454672384 |
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| author | Tingwei Zhang Peng Zhang Paris Kalathas Guangxin Wang Huaping Liu |
| author_facet | Tingwei Zhang Peng Zhang Paris Kalathas Guangxin Wang Huaping Liu |
| author_sort | Tingwei Zhang |
| collection | DOAJ |
| description | Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal’s angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low. |
| first_indexed | 2024-03-10T01:17:13Z |
| format | Article |
| id | doaj.art-86a5f4e78d38457083ea964671c90965 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:17:13Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-86a5f4e78d38457083ea964671c909652023-11-23T14:07:53ZengMDPI AGSensors1424-82202022-08-012217640410.3390/s22176404A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSITingwei Zhang0Peng Zhang1Paris Kalathas2Guangxin Wang3Huaping Liu4School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USASchool of Software & Microelectronics, Peking University, Beijing 100871, ChinaSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USASchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USASchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USARanging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal’s angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low.https://www.mdpi.com/1424-8220/22/17/6404machine learningANNAOARSSIindoor positioning |
| spellingShingle | Tingwei Zhang Peng Zhang Paris Kalathas Guangxin Wang Huaping Liu A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI Sensors machine learning ANN AOA RSSI indoor positioning |
| title | A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI |
| title_full | A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI |
| title_fullStr | A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI |
| title_full_unstemmed | A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI |
| title_short | A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI |
| title_sort | machine learning approach to improve ranging accuracy with aoa and rssi |
| topic | machine learning ANN AOA RSSI indoor positioning |
| url | https://www.mdpi.com/1424-8220/22/17/6404 |
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