Method and System of Crowd- Sourced Magnetic Fingerprinting with Neural Network Re-Training

A method and system of crowd-sourced magnetic fingerprinting for mobile device indoor navigation with neural network re-training. The method comprises determining, at a plurality of locations, a set of magnetic input parameters in accordance with a magnetic infrastructure profile of a portion of an indoor area, the processor implementing an input layer of a neural network, the set of magnetic input parameters providing a magnetic feature input to the input layer of the neural network; receiving, from a mobile device positioned at the first location, a set of measured magnetic parameters at respective ones of the plurality of locations; computing a route segment similarity factor for the mobile device based at least in part on the measured magnetic parameters profile of a corresponding route segment accessible from the magnetic fingerprint dataset; if the similarity factor exceeds a threshold similarity factor, adding the magnetic measured parameters as measured by the mobile device to the magnetic fingerprint dataset; for at least a second location along the route, receiving at least a second set of magnetic measured parameters of the mobile device; 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 magnetic feature input based on the magnetic fingerprint dataset to a magnetic feature output in accordance with the at least a second set of measured parameters; and recursively adjusting the initial weights matrix by backpropogation to diminish the error matrix until the magnetic feature output matches the at least a second set of magnetic measured parameters.