Coarse Location Estimation for Mobile Devices

• US 20160062949A1
• Filed: 09/05/2014
• Published: 03/03/2016
• Est. Priority Date: 08/29/2014
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Smith, Brian Stephen, Huang, Joseph Ding-Jiu, Veygman, Ilya K., Mayor, Robert, Millman, David Benjamin, Patel, Abhinav R., Lovich, Vitali
• Granted Patent: US 10,681,666 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 19/48 by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
• CPC Class Codes: G01S 5/0273 using multipath or indirect path propagation signals in position determination
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Coarse location estimation for mobile devices is disclosed for detecting mobile device presence at general locations of interest and switching operating modes and services for one or more location context aware applications. In some implementations, sensor data is received from a plurality of data sources at a location. For each data source, a first probability is estimated that the mobile device is at the location based on sensor data from the data sources. A second probability is estimated that the mobile device is not at the location based on sensor data from the data sources. The first and second estimated probabilities are statistically combined to generate a third estimated probability that the mobile device is at the location.

Claims

1. A method comprising:

receiving, by a computing device operating at a location, sensor data from a plurality of data sources;

determining a first estimated probability that the computing device is at the location based on first sensor data from a first data source and a first data source model;

determining a second estimated probability that the computing device is at the location based on second sensor data and a second data source model;

combining the first and second estimated probabilities; and

generating a course estimate of the location of the computing device based on the combined first and second estimated probabilities.

2. The method of claim 1, where combining the first and second estimated probabilities further comprises:

combining the first and second estimated probabilities in a Bayesian classifier.

3. The method of claim 2, where the first data source is a wireless network data source and the second data source is a satellite-based data source.

4. The method of claim 3, where the first estimated probability is given by

5. The method of claim 1, further comprising:

determining a third estimated probability that the computing device is at the location based on third sensor data and a third data source model;

combining the first, second and third estimated probabilities; and

generating a course estimate of the location of the computing device based on the combined first, second and third estimated probabilities.

6. The method of claim 5, where combining the first, second and third estimated probabilities further comprises:

combining the first, second and third estimated probabilities in a Bayesian classifier.

7. The method of claim 6, where the third data source is a particle filter and the third estimated probability is given by

8. The method of claim 1, where generating a course estimate of the location of the computing device based on the combined first and second estimated probabilities further comprises:

determining a maximum likelihood that the mobile device is at the location based on the first and second estimated probabilities.

9. The method of claim 5, where generating a course estimate of the location of the computing device based on the combined first, second and third estimated probabilities further comprises:

determining a maximum likelihood that the computing device is at the location based on the first, second and third estimated probabilities.

10. A computing device, comprising:

one or more processors;

memory coupled to the one or more processors and storing instructions, which, when executed by the one or more processors, causes the one or more processors to perform operations comprising;

receiving sensor data from a plurality of data sources;

determining a first estimated probability that the computing device is at a location based on first sensor data from a first data source and a first data source model;

determining a second estimated probability that the computing device is at the location based on second sensor data and a second data source model;

combining the first and second estimated probabilities; and

generating a course estimate of the location of the computing device based on the combined first and second estimated probabilities.

11. The computing device of claim 10, where combining the first and second estimated probabilities further comprises:

combining the first and second estimated probabilities in a Bayesian classifier.

12. The computing device of claim 11, where the first data source is a wireless network data source and the second data source is a satellite-based data source.

13. The computing device of claim 12, where the first estimated probability is given by

14. The computing device of claim 11, further comprising:

determining a third estimated probability that the computing device is at the location based on third sensor data and a third data source model;

combining the first, second and third estimated probabilities; and

generating a course estimate of the location of the computing device based on the combined first, second and third estimated probabilities.

15. The computing device of claim 14, where combining the first, second and third estimated probabilities further comprises:

combining the first, second and third estimated probabilities in a Bayesian classifier.

16. The computing device of claim 15, where the third data source is a particle filter and the third estimated probability is given by

17. The computing device of claim 11, where generating a course estimate of the location of the computing device based on the combined first and second estimated probabilities further comprises:

determining a maximum likelihood that the mobile device is at the location based on the first and second estimated probabilities.

18. The computing device of claim 17, where generating a course estimate of the location of the computing device based on the combined first, second and third estimated probabilities further comprises:

determining a maximum likelihood that the computing device is at the location based on the first, second and third estimated probabilities.

19. A non-transitory, computer-readable storage medium storing instructions, which, when executed by one or more processors, causes the one or more processors to perform operations comprising:

receiving sensor data from a plurality of data sources;

determining a first estimated probability that the computing device is at a location based on first sensor data from a first data source and a first data source model;

determining a second estimated probability that the computing device is at the location based on second sensor data and a second data source model;

combining the first and second estimated probabilities; and

generating a course estimate of the location of the computing device based on the combined first and second estimated probabilities.

20. The computer-readable storage medium of claim 19, where combining the first and second estimated probabilities further comprises:

combining the first and second estimated probabilities in a Bayesian classifier.