|Abstract:||The following paper, presents a supervised, deep-learning, neural network approach for range estimation based on IEEE 802.11 wireless local area network (WLAN) round-trip timing (RTT) measurements. The range estimation accuracy is compared against a standard, time-of-arrival (TOA), maximum-likelihood estimation (MLE)-based range estimation. The deep-learning approach is based on a “Siamese”, artificial neural network (ANN), which was trained using both indoor channel simulation, as well as actual channel measurements collected in a real, indoor office environment. Both the MLE and ANN range estimators were tested using real-channel measurements and the estimation accuracy was analyzed using “ground-truth” information collected using a LiDAR system. It is shown that the ANN-based approach outperforms the accuracy achieved by the classical MLE approach.|
Proceedings of the 2019 International Technical Meeting of The Institute of Navigation
January 28 - 31, 2019
Hyatt Regency Reston
|Pages:||435 - 444|
|Cite this article:||
Dvorecki, Nir, Bar-Shalom, Ofer, Banin, Leor, Amizur, Yuval, "A Machine Learning Approach for Wi-Fi RTT Ranging," Proceedings of the 2019 International Technical Meeting of The Institute of Navigation, Reston, Virginia, January 2019, pp. 435-444.
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