/Inference Machines for Supervised Bluetooth Localization

Inference Machines for Supervised Bluetooth Localization

Tatsuya Ishihara, Kris M. Kitani, Chieko Asakawa and Michitaka Hirose
Conference Paper, International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017, March, 2017

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State space models, such as Kalman filters or Particle filters, have been applied to improve the accuracy of radio-wave based localization. However, these models can drift radically when assumptions of the models are violated, and they do not
have a mechanism to fix errors. Therefore, we propose an approach to apply supervised learning to pedestrian localization,
which is based on the Inference Machines framework. During training, we collect localization ground truths using computer
vision while also collecting Bluetooth signals to train a state space model for localization, which can recover from
model drift. During testing, our proposed approach uses only Bluetooth signals. Our experimental results show that our approach can improve the accuracy of Bluetooth-based localization with a small number of training examples. Moreover,
our multi-modal supervision can also be used to estimate additional parameters, such as device rotation, from Bluetooth
signals that do not have such information.

BibTeX Reference
author = {Tatsuya Ishihara and Kris M. Kitani and Chieko Asakawa and Michitaka Hirose},
title = {Inference Machines for Supervised Bluetooth Localization},
booktitle = {International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2017},
year = {2017},
month = {March},
publisher = {IEEE},
keywords = {Inference Machines, State Space Model, Bluetooth Localization, Structure from Motion},