US 10,716,089 B1
Deployment of trained neural network based RSS fingerprint dataset
Sean Huberman, Guelph (CA); Joshua Karon, Toronto (CA); and Henry L. Ohab, Toronto (CA)
Assigned to MAPSTED CORP., Mississauga, Ontario (CA)
Filed by Mapsted Corp., Mississauga (CA)
Filed on Jun. 3, 2019, as Appl. No. 16/429,551.
Int. Cl. H04W 64/00 (2009.01); H04B 17/318 (2015.01); H04B 17/391 (2015.01)
CPC H04W 64/003 (2013.01) [H04B 17/318 (2015.01); H04B 17/391 (2015.01); H04W 64/006 (2013.01)] 20 Claims
OG exemplary drawing
1. A method, executed in a processor of a server computing device, of deploying received signal strength (RSS) fingerprint dataset, based on a trained neural network for mobile device indoor navigation, the method comprising:
receiving RSS parameters from a plurality of mobile devices, the RSS parameters acquired for a set of positions within an indoor area, the RSS parameters being determined by the plurality of mobile devices using a respective RSS sensor device;
training a neural network implemented in the processor at lease in part based on the RSS parameters, the neural network comprising a first neural network layer corresponding to a set of RSS input parameters for a wireless signal in accordance with a first wireless communication protocol, and at least a second neural network layer corresponding to the set of RSS input parameters for the wireless signal in accordance with at least a second wireless communication protocol, an RSS input parameter being based on a postulated RSS model;
when a density of points represented by the set of positions having the RSS parameters exceeds a deployment threshold density, deploying the RSS fingerprint dataset within a fingerprint map, based on the trained neural network, the fingerprint map encompassing the set of positions; and
navigating another mobile device in the indoor area using the deployed RSS fingerprint dataset within the fingerprint map.