1. A method, executed in a processor of a server computing device, of maintaining a trained neural network for mobile device indoor navigation and positioning, the method comprising: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, the set of RSS input parameters providing an RSS input feature to the input layer of the neural network, wherein the neural network comprises,
a first neural network layer corresponding to the 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 that corresponds to the set of RSS input parameters for the wireless signal in accordance with at least a second wireless communication protocol;
receiving a set of RSS measured parameters acquired at a mobile device positioned at the first location from the wireless signal source at the second location;
computing, at an output layer of the trained neural network, an output error based on comparing the RSS input feature to an RSS output feature generated at the output layer, the RSS output feature being generated at least in part based on a matrix of weights associated with at least one of the first and the at least a second neural network layer; and
if the output error exceeds a threshold value, re-training the neural network based at least in part upon re-initializing the matrix of weights associated with the one of the first and the at least a second neural network layer.