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A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI
Tingwei Zhang1, Peng Zhang2, Paris Kalathas1
1School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA.
Abstract:
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.
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