Systems and methods for localization
Systems and methods for a localization system are provided. In one aspect, a RF signature map for a geographical area is determined using a Gaussian Process (“GP”) model. Training RF measurements are taken at some locations within the area to train the GP using the Firefly Algorithm (“FA”). The RF measurements for other locations of the area are predicted using the conditional probabilities of the trained GP and without taking RF measurements at those other locations. The RF signature map is used for fingerprinting localization. In one aspect, a reference RF signature map is constructed for one, some, or all access points (“APs”) covering the area. A location of a user device, such as, for example, a smart phone, is then estimated by comparing the RF signals received by the user device from one or more APs with the determined one or more reference RF signature maps using a combined likelihood function.
1. An apparatus for a localization system, the apparatus comprising:
a processor configured to:
determine training RF signal data measured at a number of training locations within a geographical area, the training RF signal data being measured at the number of training locations for RF signals transmitted by at least one wireless transmitter covering the geographical area;
determine, by applying the training RF signal data to a Firefly Algorithm, one or more parameters of a Gaussian Process model;
estimate, using the one or more parameters determined using the Firefly Algorithm and the Gaussian Process model, mean or variance RF signal values for predicting RF signal data at a number of other locations within the geographical area; and,
generate, using the training RF signal data and the mean or variance RF values determined using the Firefly Algorithm and the Gaussian Process model, at least one RF signature map for the geographical area for RF signals transmitted by the at least one wireless transmitter.
2. The apparatus of claim 1 , wherein the processor is further configured to:
estimate a location of a device within the geographical area using RF signal data received by the device from the at least one wireless transmitter and the at least one RF signature map generated by the processor.
3. The apparatus of claim 2 , wherein the processor is further configured to:
estimate the location of the device as a determined nearest neighbor location within the geographical area.
4. The apparatus of claim 2 , wherein the processor is further configured to:
determine at least one likelihood function for accounting for at least one variance; and,
estimate the location of the device using the determined at least one likelihood function.
5. The apparatus of claim 1 , wherein the processor is configured to generate the RF signature map for the geographical area without measuring RF signal data at the other locations of the geographical area.
6. The apparatus of claim 1 , wherein the at least one RF signature map generated by the processor includes at least one estimated mean and variance RF signal value for at least one of the number of other locations within the geographical area.
7. The apparatus of claim 1 , wherein the processor is configured to determine RF signal strength data for the determined training RF signal data.
8. A computer-implemented method for localization of a user device in a geographical area, the method comprising:
determining training RF signal data measured at a number of training locations within the geographical area, the training RF signal data being measured at the number of training locations for RF signals transmitted by at least one wireless transmitter covering the geographical area;
applying the training RF signal data to a Firefly Algorithm and computing, using a processor, one or more parameters of a Gaussian Process model;
estimating, using the one or more parameters determined using the Firefly Algorithm and the Gaussian Process model, mean or variance RF signal values for predicting RF signal data at a number of other locations within the geographical area; and,
generating, using the training RF signal data and the mean or variance RF signal values determined using the Firefly Algorithm and the Gaussian Process model, at least one RF signature map for the geographical area for RF signals transmitted by the at least one wireless transmitter.
9. The computer-implemented method of claim 8 , further comprising:
estimating a location of the user device within the geographical area using RF signal data received by the device from the at least one wireless transmitter and the at least one RF signature map generated by the processor.
10. The computer-implemented method of claim 9 , wherein estimating the location of the user device further comprises determining a nearest neighbor location within the geographical area.
11. The computer-implemented method of claim 9 , further comprising:
determining at least one likelihood function for accounting for at least one variance; and,
estimating the location of the device using the determined at least one likelihood function.
12. The computer-implemented method of claim 8 , generating the RF signature map for the geographical area without measuring RF signal data at the other locations of the geographical area.
13. The computer-implemented method of claim 8 , wherein generating the at least one RF signature map further comprises computing at least one estimated mean and variance RF signal value for at least one of the number of other locations within the geographical area.
14. The computer-implemented method of claim 8 , wherein determining the training RF signal data includes determining RF signal strength data from the RF signal data.