Semi-supervised variational autoencoder for indoor localization
US11373096B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jun 18, 2020 |
| Grant date | Jun 28, 2022 |
| Priority date | — |
| Expiry date | Feb 22, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of training a predictor to predict a location of a computing device in an indoor environment incudes: receiving training data including strength of signals received from wireless access points at positions of an indoor environment, where the training data includes: a subset of labeled data including signal strength values and location labels; and a subset of unlabeled data including signal strength values and not including labels indicative of locations; training a variational autoencoder to minimize a reconstruction loss of the signal strength values of the training data, where the variational autoencoder includes encoder neural networks and decoder neural networks; and training a classification neural network to minimize a prediction loss on the labeled data, where the classification neural network generates a predicted location based on the latent variable, and where the encoder neural networks and the classification neural network form the predictor.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.