LOCATION DETERMINATION USING FINGERPRINT DATA

• US 20140171114A1
• Filed: 12/14/2012
• Published: 06/19/2014
• Est. Priority Date: 12/14/2012
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Marti, Lukas M., Mayor, Robert, Ma, Shannon M.
• Granted Patent: US 8,934,921 B2
• Time in Patent Office: Days
• US Class Current: 455/456.2
• CPC Class Codes: G01C 21/10 by using measurements of speed or acceleration G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01S 2205/02 Indoor
• CPC Class Codes: G01S 5/017 Detecting state or type of motion
• CPC Class Codes: G01S 5/02521 using a radio-map
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Methods, program products, and systems for using a location fingerprint database to determine a location of a mobile device are described. A mobile device can use location fingerprint data received from a server to determine a location of the mobile device at the venue. The mobile device can obtain, from a sensor of the mobile device, a vector of sensor readings, each sensor reading can measure an environment variable, e.g., a signal received by the sensor from a signal source. The mobile device can perform a statistical match between the vector and the location fingerprint data. The mobile device can then estimate a current location of the mobile device based on the statistical match.

Claims

1. A method comprising:

receiving, by a mobile device and from a server, location fingerprint data, the location fingerprint data comprising a plurality of locations at a venue and a fingerprint of each location, the venue comprising a space accessible by a pedestrian and one or more constraints of movement of the pedestrian, each fingerprint comprising one or more expected measurements of an environment variable at the corresponding location;

receiving a measurement from one or more sensors of the mobile device, the measurement comprising one or more readings of the one or more sensors on the environment variable; and

determining a location of the mobile device at the venue using the measurement and the location fingerprint data, including performing a statistical classification categorizing the measurement into one or more possible locations where the measurement can be received by the one or more sensors, each possible location being associated with a probability that the measurement matches a fingerprint of the respective possible location.

2. The method of claim 1, wherein:

each environment variable comprises a signal from a signal source detectable by the mobile device, the one or more signal sources include at least one of a wireless access point, a light source, a sound source, a magnetic source, or a heat source; and

the one or more expected measurements include at least one of a received signal strength indication (RSSI), a round-trip time, a magnetic field strength, a temperature, a noise level, or an air pressure level.

3. The method of claim 1, wherein receiving the location fingerprint data comprises:

requesting, by the mobile device, the location fingerprint data from the server when the mobile device determines that the mobile device is located in proximity to the venue based on a threshold; and

receiving the location fingerprint data in response.

4. The method of claim 1, wherein performing the statistical classification comprises:

categorizing a sample vector into one or more categories each being associated with a probability based on training data, wherein;

the measurement is designated as the sample vector,

the location fingerprint data are designated as the training data,

the one or more categories each corresponds to a possible location at the venue, and

the probability associated with each category indicates a likelihood that the mobile device is located at the possible location corresponding to the category.

5. The method of claim 4, comprising determining the probability associated with each category, wherein determining the probability comprises:

determining a point-wise kernel estimation of a probability density of the measurement is measured at each of the possible locations, the point-wise kernel estimation having a kernel function, the kernel function being applied to a difference between the measurement and a fingerprint of the respective possible location, the kernel function being a symmetric function that integrates to one.

6. The method of claim 5, wherein the kernel function includes a Gaussian function.

7. The method of claim 5, wherein the kernel function is applied to an a priori domain provided by a particle filter.

8. The method of claim 1, wherein determining the location of the mobile device comprises aggregating statistics of a group of possible locations.

9. The method of claim 8, wherein determining the location of the mobile device comprises calculating the location using the group of locations based on a context, the context including one or more readings of at least one of an accelerometer, a magnetic sensor, a gyroscope, or a barometer.

10. A system comprising:

a mobile device;

a non-transitory computer-readable medium storing instructions operable to cause the mobile device to perform operations comprising;

receiving, by a mobile device and from a server, location fingerprint data, the location fingerprint data comprising a plurality of locations at a venue and a fingerprint of each location, the venue comprising a space accessible by a pedestrian and one or more constraints of movement of the pedestrian, each fingerprint comprising one or more expected measurements of an environment variable at the corresponding location;

receiving a measurement from one or more sensors of the mobile device, the measurement comprising one or more readings of the one or more sensors on the environment variable; and

determining a location of the mobile device at the venue using the measurement and the location fingerprint data, including performing a statistical classification categorizing the measurement into one or more possible locations where the measurement can be received by the one or more sensors, each possible location being associated with a probability that the measurement matches a fingerprint of the respective possible location.

11. The system of claim 10, wherein:

each environment variable comprises a signal from a signal source detectable by the mobile device, the one or more signal sources include at least one of a wireless access point, a light source, a sound source, a magnetic source, or a heat source; and

the one or more expected measurements include at least one of a received signal strength indication (RSSI), a round-trip time, a magnetic field strength, a temperature, a noise level, or an air pressure level.

12. The system of claim 10, wherein receiving the location fingerprint data comprises:

requesting, by the mobile device, the location fingerprint data from the server when the mobile device determines that the mobile device is located in proximity to the venue based on a threshold; and

receiving the location fingerprint data in response.

13. The system of claim 10, wherein performing the statistical classification comprises:

categorizing a sample vector into one or more categories each being associated with a probability based on training data, wherein;

the measurement is designated as the sample vector,

the location fingerprint data are designated as the training data,

the one or more categories each corresponds to a possible location at the venue, and

the probability associated with each category indicates a likelihood that the mobile device is located at the possible location corresponding to the category.

14. The system of claim 13, comprising determining the probability associated with each category, wherein determining the probability comprises:

determining a point-wise kernel estimation of a probability density of the measurement is measured at each of the possible locations, the point-wise kernel estimation having a kernel function, the kernel function being applied to a difference between the measurement and a fingerprint of the respective possible location, the kernel function being a symmetric function that integrates to one.

15. The system of claim 14, wherein the kernel function includes a Gaussian function.

16. The system of claim 14, wherein the kernel function is applied to an a priori domain provided by a particle filter.

17. The system of claim 10, wherein determining the location of the mobile device comprises aggregating statistics of a group of possible locations.

18. The system of claim 17, wherein determining the location of the mobile device comprises calculating the location using the group of locations based on a context, the context including one or more readings of at least one of an accelerometer, a magnetic sensor, a gyroscope, or a barometer.

19. A non-transitory computer-readable medium storing instructions operable to cause one or more processors to perform operations comprising:

receiving, by a mobile device and from a server, location fingerprint data, the location fingerprint data comprising a plurality of locations at a venue and a fingerprint of each location, the venue comprising a space accessible by a pedestrian and one or more constraints of movement of the pedestrian, each fingerprint comprising one or more expected measurements of an environment variable at the corresponding location;

receiving a measurement from one or more sensors of the mobile device, the measurement comprising one or more readings of the one or more sensors on the environment variable; and

determining a location of the mobile device at the venue using the measurement and the location fingerprint data, including performing a statistical classification categorizing the measurement into one or more possible locations where the measurement can be received by the one or more sensors, each possible location being associated with a probability that the measurement matches a fingerprint of the respective possible location.

20. The non-transitory computer-readable medium of claim 19, wherein:

each environment variable comprises a signal from a signal source detectable by the mobile device, the one or more signal sources include at least one of a wireless access point, a light source, a sound source, a magnetic source, or a heat source; and

the one or more expected measurements include at least one of a received signal strength indication (RSSI), a round-trip time, a magnetic field strength, a temperature, a noise level, or an air pressure level.

21. The non-transitory computer-readable medium of claim 19, wherein receiving the location fingerprint data comprises:

requesting, by the mobile device, the location fingerprint data from the server when the mobile device determines that the mobile device is located in proximity to the venue based on a threshold; and

receiving the location fingerprint data in response.

22. The non-transitory computer-readable medium of claim 19, wherein performing the statistical classification comprises:

categorizing a sample vector into one or more categories each being associated with a probability based on training data, wherein;

the measurement is designated as the sample vector,

the location fingerprint data are designated as the training data,

the one or more categories each corresponds to a possible location at the venue, and

the probability associated with each category indicates a likelihood that the mobile device is located at the possible location corresponding to the category.

23. The non-transitory computer-readable medium of claim 22, comprising determining the probability associated with each category, wherein determining the probability comprises:

determining a point-wise kernel estimation of a probability density of the measurement is measured at each of the possible locations, the point-wise kernel estimation having a kernel function, the kernel function being applied to a difference between the measurement and a fingerprint of the respective possible location, the kernel function being a symmetric function that integrates to one.

24. The non-transitory computer-readable medium of claim 23, wherein the kernel function includes a Gaussian function.

25. The non-transitory computer-readable medium of claim 23, wherein the kernel function is applied to an a priori domain provided by a particle filter.

26. The non-transitory computer-readable medium of claim 19, wherein determining the location of the mobile device comprises aggregating statistics of a group of possible locations.

27. The non-transitory computer-readable medium of claim 26, wherein determining the location of the mobile device comprises calculating the location using the group of locations based on a context, the context including one or more readings of at least one of an accelerometer, a magnetic sensor, a gyroscope, or a barometer.