Patent Document ID: 20160077191
Application ID: 14843961
Patent Status: 0

Claim One:
1. A method, comprising: determining, by a computing device, a plurality of trained Gaussian processes that model signals emitted by a plurality of wireless signal emitters, each Gaussian process of the plurality of trained Gaussian processes based on one or more hyperparameters, wherein the plurality of trained Gaussian processes comprises a first Gaussian process and a second Gaussian process, wherein the first Gaussian process is based on first hyperparameter values of the one or more hyperparameters related to a first wireless signal emitter of the plurality of wireless signal emitters, and wherein the second Gaussian process is based on second hyperparameter values of the one or more hyperparameters related to a second wireless signal emitter of the plurality of wireless signal emitters; determining, by the computing device, a set of comparison hyperparameters from the one or more hyperparameters; determining, by the computing device, a first set of comparison hyperparameter values of the first hyperparameter values and a second set of comparison hyperparameter values of the second hyperparameter values; determining, by the computing device, whether the first set of comparison hyperparameter values are within one or more threshold values of the second set of comparison hyperparameter values; after determining that the first set of comparison hyperparameter values are within the one or more threshold values of the second set of comparison hyperparameter values, determining by the computing device, that the first Gaussian process and the second Gaussian process are dependent Gaussian processes; after determining that the first Gaussian process and the second Gaussian process are dependent Gaussian processes, determining by the computing device, a representative Gaussian process based on the first Gaussian process and the second Gaussian process; and providing an estimated-location output of the computing device based on the representative Gaussian process.