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.
2. The method of claim 1, wherein providing the estimated-location output comprises:
receiving a request related to locating a mobile device;
determining an estimate of the location of the mobile device based on the representative Gaussian process;
generating the estimated-location output comprising the estimate of the location; and
providing the estimated-location output.
3. The method of claim 1, further comprising:
after determining, for an outlying comparison hyperparameter of the set of comparison hyperparameters, that a first outlying comparison hyperparameter value of the first set of comparison hyperparameter values is not within a corresponding threshold outlying comparison hyperparameter value of a second outlying comparison hyperparameter value of the second set of hyperparameter values, determining by the computing device that the first Gaussian process is independent of the second Gaussian process.
4. The method of claim 1, wherein determining by the computing device the representative Gaussian process comprises:
determining first signal-strength measurements used to train the first Gaussian process;
determining second signal-strength measurements used to train the second Gaussian process, wherein the first signal-strength measurements differ from the second signal-strength measurements; and
training the representative Gaussian process using both the first signal-strength measurements and the second signal-strength measurements.
5. The method of claim 4, wherein providing the estimated-location output comprises:
after training the representative Gaussian process, providing the estimated-location output based on the representative Gaussian process.
6. The method of claim 1, wherein the one or more hyperparameters are selected from the group of hyperparameters consisting of: a location hyperparameter, a power-output hyperparameter, a signal-attenuation hyperparameter, and a noise hyperparameter.
7. The method of claim 6, wherein the location hyperparameter comprises a latitude hyperparameter and a longitude hyperparameter.
8. The method of claim 6, wherein the noise hyperparameter comprises a background-noise hyperparameter and a noise-confidence hyperparameter.
9. The method of claim 1, wherein the first wireless signal emitter is identified using a first wireless-signal-emitter identifier, wherein the second wireless signal emitter is identified using a second wireless-signal-emitter identifier, wherein the first Gaussian process is associated with the first wireless-signal-emitter identifier, and wherein the second Gaussian process is associated with the second wireless-signal-emitter identifier.
10. The method of claim 9, wherein determining, by the computing device, the representative Gaussian process comprises:
determining first signal-strength measurements used to train the first Gaussian process;
determining second signal-strength measurements used to train the second Gaussian process, wherein the first signal-strength measurements differ from the second signal-strength measurements;
training the representative Gaussian process using both the first signal-strength measurements and the second signal-strength measurements; and
after training the representative Gaussian process, associating the representative Gaussian process with the first wireless-signal-emitter identifier and the second wireless-signal-emitter identifier.
11. The method of claim 9, wherein at least one wireless-signal-emitter identifier of the first wireless-signal-emitter identifier and the second wireless-signal-emitter identifier comprises a Basic Service Set Identifier (BSSID).
12. A computing device, comprising:
one or more processors; and
data storage, configured to store at least computer-readable program instructions, wherein the instructions are configured to, upon execution by the one or more processors, cause the computing device to perform functions comprising:
determining 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 a set of comparison hyperparameters from the one or more hyperparameters;
determining 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 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 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 a representative Gaussian process based on the first Gaussian process and the second Gaussian process; and
providing an estimated-location output based on the representative Gaussian process.
13. The computing device of claim 12, wherein providing the estimated-location output comprises:
receiving a request related to locating a mobile device;
determining an estimate of the location of the mobile device based on the representative Gaussian process;
generating the estimated-location output comprising the estimate of the location; and
providing the estimated-location output.
14. The computing device of claim 12, wherein the functions further comprise:
after determining, for an outlying comparison hyperparameter of the set of comparison hyperparameters, that the first outlying comparison hyperparameter value of the first hyperparameter values is not within a corresponding threshold outlying comparison hyperparameter value of the second outlying comparison hyperparameter value of the second hyperparameter values, determining that the first Gaussian process is independent of the second Gaussian process.
15. The computing device of claim 12, wherein determining the representative Gaussian process comprises:
determining first signal-strength measurements used to train the first Gaussian process;
determining second signal-strength measurements used to train the second Gaussian process, wherein the first signal-strength measurements differ from the second signal-strength measurements; and
training the representative Gaussian process using both the first signal-strength measurements and the second signal-strength measurements.
16. The computing device of claim 12, wherein the one or more hyperparameters are selected from the group of hyperparameters consisting of: a location hyperparameter, a power-output hyperparameter, a signal-attenuation hyperparameter, and a noise hyperparameter.
17. The computing device of claim 12, wherein the first wireless signal emitter is identified using a first wireless-signal-emitter identifier, wherein the second wireless signal emitter is identified using a second wireless-signal-emitter identifier, wherein the first Gaussian process is associated with the first wireless-signal-emitter identifier, and wherein the second Gaussian process is associated with the second wireless-signal-emitter identifier.
18. The computing device of claim 17, wherein determining the representative Gaussian process comprises:
determining first signal-strength measurements used to train the first Gaussian process;
determining second signal-strength measurements used to train the second Gaussian process, wherein the first signal-strength measurements differ from the second signal-strength measurements;
training a merged Gaussian process using both the first signal-strength measurements and the second signal-strength measurements; and
after training the merged Gaussian process, associating the merged Gaussian process with the first wireless-signal-emitter identifier and the second wireless-signal-emitter identifier.
19. An article of manufacture including a computer-readable storage medium having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform functions comprising:
determining 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 a set of comparison hyperparameters from the one or more hyperparameters;
determining 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 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 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 a representative Gaussian process based on the first Gaussian process and the second Gaussian process; and
providing an estimated-location output based on the representative Gaussian process.
20. The article of manufacture of claim 19, wherein providing the estimated-location output comprises:
receiving a request related to locating a mobile device;
determining an estimate of the location of the mobile device based on the representative Gaussian process;
generating the estimated-location output comprising the estimate of the location; and
providing the estimated-location output.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1-4. (canceled)
5. Position measuring device, comprising:
a rotary laser beam transmitter which is located at the center of a polar coordinate system, the rotary laser beam transmitter being rotatable about said center in a fixed plane at a given constant rotary speed, and being adapted to emit at least one rotary laser beam;
at least one photosensitive position sensor mounted in the path of said at least one rotary laser beam which delivers an electrical pulse which is representative of the length in time and phase angle during illumination thereof by the rotating laser beam; and
means for determining a measure of the angular position and the radial distance of the sensor in the indicated polar coordinate system from the phase angle and length in time of the pulses delivered by said at least one photosensitive position sensor.
6. Position measuring device as claimed in claim 5, wherein the laser transmitter delivers a pulsed laser beam for producing a pulse train comprised of a plurality of individual pulses on the at least one photosensitive position sensor.
7. Position measuring device as claimed in claim 6, wherein the photosensitive position sensor is one of a position sensing diode (PSD) and a pixel-oriented sensor of one of a CMOS and CCD construction.
8. Position measuring device as claimed in claim 5, wherein the photosensitive position sensor is one of a position sensing diode (PSD) and a pixel-oriented sensor of one of a CMOS and CCD construction.