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A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI
| Content Provider | MDPI |
|---|---|
| Author | Zhang, Tingwei Zhang, Peng Wang, Guangxin Liu, Huaping |
| Copyright Year | 2022 |
| 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. |
| Starting Page | 6404 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22176404 |
| Journal | Sensors |
| Issue Number | 17 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-25 |
| Access Restriction | Open |
| Subject Keyword | Sensors Machine Learning Ann Aoa Rssi Indoor Positioning |
| Content Type | Text |
| Resource Type | Article |
