Neural network training for mobile device RSS fingerprint-based indoor navigation
US10422854B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | May 1, 2019 |
| Grant date | Sep 24, 2019 |
| Priority date | — |
| Expiry date | May 1, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/046
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A method and system of neural network training for mobile device indoor navigation and positioning. The method, executed in a processor of a server computing device, comprises determining, in the processor, at a first location relative to a wireless signal source at a second location, a set of received signal strength (RSS) input parameters in accordance with a postulated RSS model, the processor implementing an input layer of a neural network, receiving, from a mobile device positioned at the first location, a set of RSS measured parameters from the wireless signal source at the second location, computing, at an output layer of the neural network implemented by the processor, an error matrix based on comparing an initial matrix of weights associated with the at least a first neural network layer representing the RSS input feature to an RSS output feature in accordance with the RSS measured parameters of the mobile device at the first location, and recursively adjusting the initial weights matrix by backpropogation to diminish the error matrix until the generated RSS output feature matches the RSS measured parameters.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.