Neural network—instantiated lightweight calibration of RSS fingerprint dataset
US10641610B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 3, 2019 |
| Grant date | May 5, 2020 |
| Priority date | — |
| Expiry date | Jun 3, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W4/80
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A method and system of instantiating a lightweight re-calibration of a received signal strength (RSS) fingerprint dataset for mobile device indoor navigation. The method comprises, based on RSS parameters acquired from a plurality of mobile devices acquired at a set of positions within an indoor area, accumulating the RSS parameters in accordance with a trained neural network-based RSS fingerprint dataset in a fingerprint database of the indoor area; identifying respective positions of a subset of the set of positions having a variance that exceeds a threshold variance between observed RSS parameters and RSS parameters determined in accordance the trained neural network; and when contiguous positions of the subset are encompassed by a boundary representing a portion of the indoor area, instantiating a re-calibration of the RSS fingerprint dataset for mobile device navigation within the portion of the indoor area.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.