Indoor Wireless Localization Using Consumer-Grade 60 GHz Equipment with Machine Learning for Intelligent Material Handling

Wireless indoor localization is critical for autonomous agents in modern and future smart warehouses. Millimeter-wave (mmWave) frequencies have been investigated for high-precision localization in recent years for indoor as well as outdoor positioning. We propose machine learning (ML) techniques ove...

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Bibliographic Details
Main Authors: Vashist, Abhishek, Bhanushali, Darshan Ramesh, Relyea, Robert, Hochgraf, Clark, Ganguly, Amlan, Sai Manoj, P D, Ptucha, Raymond, Kwasinski, Andres, Kuhl, Michael E.
Format: Conference Proceeding
Language:English
Subjects:
Data collection
Data models
indoor localization
Machine learning
Millimeter-wave
Signal to noise ratio
Training
Wireless communication
Wireless sensor networks
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Summary:Wireless indoor localization is critical for autonomous agents in modern and future smart warehouses. Millimeter-wave (mmWave) frequencies have been investigated for high-precision localization in recent years for indoor as well as outdoor positioning. We propose machine learning (ML) techniques over a radio map to estimate the location of an autonomous material handling agent used in warehouses. Based on our experimental results we demonstrate that a Multilayer Perceptron (MLP) based positioning achieves centimeter level accuracy with Root Mean Square Error (RMSE) of 0.84m. The proposed localization technique achieves up to 80% lower positioning error compared to state-of-the-art mmWave wireless localization techniques.
ISSN:2158-4001
DOI:10.1109/ICCE46568.2020.9043072