SYSTEMS AND METHODS FOR CALIBRATION BASED INDOOR GEOLOCATION

• US 20140018095A1
• Filed: 07/01/2013
• Published: 01/16/2014
• Est. Priority Date: 06/29/2012
• Status: Active Grant

• Current Assignee: Lighthouse Signal Systems, LLC
• Original Assignee: Lighthouse Signal Systems, LLC
• Inventors: Parvizi, Parviz, Heidari, Mohammad
• Granted Patent: US 9,439,041 B2
• Time in Patent Office: Days
• US Class Current: 455/456.1
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/33 for indoor environments, e.g. buildings

Abstract

In a system facilitating the calibration of a map-point grid for an indoor location determination, the grid includes several map points, each having a radio frequency (RF) data fingerprint being associated therewith. At least one of: (i) RF signal data from several RF sources, (ii) a user specified location indication, and (ii) tracking data from a sensor, the tracking data indicating a user'"'"'s movement relative to a base map point, are received. The map-point grid is updated based on, at least in part, at least one of (i) adjusted RF data, the received RF data being adjusted using systematic analysis thereof, (ii) the tracking data, and (iii) the location indication. A user'"'"'s location may be determined based on the fingerprints associated with the map-point grid, and sensor data.

Claims

1. A method of determining a location of a user within an indoor environment, the method comprising:

for each radio frequency (RF) source of a plurality of RF sources;

(i) receiving RF signal data from the RF source, and (ii) computing a yield of the RF source based on the received data;

selecting a subset of fingerprints from a database of fingerprints, based on, at least in part, the yield associated with each of the plurality of RF sources;

receiving tracking data from an inertial navigation system; and

determining a location of a user based on, at least in part;

(i) the RF signal data received from each of the RF sources in the subset, and (ii) the received tracking data.

2. A method of determining a location of a user within an indoor environment, the method comprising:

receiving at a first time instant, a first set of radio frequency (RF) data from a plurality of RF sources;

computing, based at least in part on the first set of RF data, a first location estimate and a second location estimate;

receiving at a second time instant;

(i) a second set of RF data from the plurality of RF sources, and (ii) tracking data from an inertial navigation system;

computing, based at least in part on the second set of RF data, a third location estimate and a fourth location estimate;

computing;

(i) a fifth location estimate based in part on the first location estimate, and (ii) a sixth location estimate based in part on the second location estimate, both the fifth and sixth location estimates also being based in part on the tracking data;

computing an error metric by comparing;

(i) the fifth location estimate with at least one of the third and fourth location estimates, and (ii) the sixth location estimate with at least one of the third and fourth location estimates; and

designating based at least in part on the error metric;

(i) one of the first and second location estimates as a location of the user at the first time instant, and (ii)one of the third and fourth location estimates as a location of the user at the second time instant.

3. The method of claim 2 further comprising:

associating an accuracy parameter with one of the first, second, third, and fourth location estimates,

wherein the error metric is based in part on the accuracy parameter.

4. The method of claim 3, wherein the accuracy parameter is based in part on at least one of a type of a building, number the RF sources, power of the RF source, types of the RF sources, yield of the RF sources, tracking data, and statistics associated with tracking data.

5. The method of claim 2 further comprising:

computing;

(i) a seventh location estimate based at least in part on the first set of RF data;

(ii) an eighth location estimate based at least in part on the second set of RF data;

(ii) a ninth location estimate based at least in part on the seventh location estimate and the tracking data,

wherein the error metric is computed based in part on at least one of the seventh, eighth, and ninth location estimates.

6. A method of calibrating a map-point grid for indoor location determination, the grid comprising a plurality of map points, each having a radio frequency (RF) data fingerprint being associated therewith, the method comprising:

receiving at least one of;

(i) RF signal data from a plurality of RF sources, (ii) a user specified location indication, and (ii) tracking data from a sensor, the tracking data indicating a user'"'"'s movement relative to a base map point; and

updating the map-point grid based on, at least in part, at least one of (i) adjusted RF data, the received RF data being adjusted using systematic analysis thereof, (ii) the tracking data, and (iii) the location indication.

7. The method of claim 6, wherein updating the map-point grid comprises:

computing an RF data fingerprint based on the RF signal data;

computing a location of the user based on, at least in part, the tracking data, the computed location being different than the base map point;

adding a new map point to the grid, the new map point being associated with the computed location and the computed RF data fingerprint.

8. The method of claim 6, wherein updating the map-point grid comprises:

computing a spatial and directional orientation of the user at the base map point with respect to at least one of the plurality of RF sources based on, at least in part, the tracking data; and

updating a fingerprint associated with a map point in the grid, based on, at least in part, both the received RF data and the computed orientation.

9. The method of claim 6, wherein updating the map-point grid comprises:

computing a location of a user based on, at least in part, the tracking data;

designating the computed location as a new map point;

associating a set of other map points with the new map point;

updating the RF signal data received at the new map point from at least one of the plurality of RF sources, based on, at least in part, RF signal data received from that RF source at each of the other map points in the set associated with the new map point;

computing an RF data fingerprint based in part on the updated RF signal data; and

associating the computed RF data fingerprint with the new map point.

10. The method of claim 9, wherein updating the received RF signal data comprises computing an average of a parameter of the received RF signal data.

11. The method of claim 10, wherein the average is a weighted average.

12. The method of claim 10, wherein the parameter is at least one of RF signal power and yield data.

13. The method of claim 9 further comprising:

receiving at the new map point, a location of a user based on, at least in part, a location confirmation signal; and

determining if the computed location is substantially same as the received location.

14. The method of claim 6, further comprising:

selecting a map point based on, at least in part, at least one of (i) the received tracking data, and (ii) the received location indication; and

associating a set of other map points with the selected map point,

wherein systematic analysis of the received RF data comprises computing a fingerprint as a function of the RF signal data associated with each of the other map points in the set; and

wherein updating the map-point grid comprises associating the computed fingerprint with the selected map point.

15. The method of claim 14, wherein the function is interpolation.

16. The method of claim 15, wherein the interpolation is one of simple averaging and weighted averaging.

17. The method of claim 6, wherein:

the grid comprises a plurality of routes, each route corresponding to a set of map points comprising a reference map point, a radio frequency (RF) data fingerprint being associated with each map point in the set of map points corresponding to each route;

receiving the user specified location indication comprises receiving from a device associated with a user, an identification of a map point and an identification of a route; and

updating the map-point grid comprises updating a weight associated with the RF data fingerprint associated with each map point in the set of map points corresponding to the identified route, if the identified map point is determined not to represent the reference map point corresponding to the identified route.

18. The method of claim 17, wherein the reference map point comprises one of a start map point, representing a starting point of the identified route, and an end map point, representing an end point of the identified route.

19. The method of claim 17, wherein receiving the identification of the route comprises receiving at least one of a designated route number and a designated route name.

20. The method of claim 17, wherein receiving the identification of the route comprises receiving a selection of the route from a specified list of routes.

21. The method of claim 17, wherein receiving the RF signal data comprises receiving, from the device associated with the user, RF data associated with each map point in the set of map points corresponding to the identified route, the fingerprint associated with each map point being based on, at least in part, the received RF data associated with the corresponding map point.

22. The method of claim 6, wherein:

the grid comprises a route corresponding to a set of map points;

receiving the RF signal data comprises receiving a plurality of calibration scans corresponding to the route, receiving each scan comprising receiving sensor data associated with each map point in the set of map points corresponding to the route; and

updating the map-point grid comprises, for each calibration scan;

determining a value of a route parameter, based on, at least in part, the sensor data corresponding to the scan;

assigning a weight to the calibration scan, based on, at least in part, a comparison of the determined value of the route parameter and a statistical value associated with the route parameter.

23. The method of claim 22, further comprising computing the statistical value associated with the route parameter, based on, at least in part, the plurality of calibration scans.

24. The method of claim 22, wherein updating the map-point grid further comprises computing for each map point corresponding to the route, an RF fingerprint, based on, at least in part, the plurality of calibration scans and the weight assigned to each calibration scan.

25. The method of claim 22, wherein the route parameter comprises at least one of a list of access points observable on the route, a power of an access point observable on the route, a yield of an access point observable on the route, a number of turns on a route, a length of a segment of the route, an orientation of a segment of the route, acceleration observable on the route, altitude associated with the route, and barometric pressure observable on the route.

26. The method of claim 6, wherein the sensor data comprises inertial navigation sensor data.

27. The method of claim 6, wherein:

the grid comprises a plurality of segments, each segment comprising a set of map points and each map point corresponds to a map location; and

the systematic analysis comprises;

designating a segment from the plurality of segments as a candidate segment;

excluding from a data set used by a location-determination engine to compute a device location, radio frequency (RF) data associated with each map point in the set of map points associated with the candidate segment;

selecting a subset of map points from the set of map points associated with the candidate segment;

determining a reference location based on, at least in part, map locations corresponding to each map point in the subset;

obtaining from the location-determination engine a location based on, at least in part, RF data associated with each map point in the subset;

adjusting in the data set used by the location-determination engine a weight associated with the candidate segment, based on, at least in part, a comparison of the obtained location with the reference location.

28. The method of claim 27, wherein the systematic analysis further comprises:

selecting a set of segments, each segment in the set comprising a map point within a specified distance from a map point corresponding to the candidate segment;

computing a fitness measure based on a comparison of a fingerprint associated with the map point corresponding to the candidate segment and map points corresponding to the segments in the selected set; and

further adjusting the weight associated with the candidate segment based on, at least in part, the fitness measure.

29. The method of claim 27, wherein the candidate segment comprises a first sub-segment and a second sub-segment connected to the first sub-segment at an angle other than 180°

.

30. The method of claim 27, wherein:

the subset consists essentially of a single map point; and

the reference location comprises the map location corresponding to the single map point.

31. The method of claim 6, wherein:

the grid comprises a plurality of segments, each segment comprising a set of map points and each map point corresponds to a map location; and

the systematic analysis comprises;

designating a segment from the plurality of segments as a candidate segment;

selecting a set of segments, each segment in the set comprising a map point within a specified distance from a map point corresponding to the candidate segment;

computing a fitness measure based on a comparison of a fingerprint associated with the map point corresponding to the candidate segment and map points corresponding to the segments in the selected set; and

adjusting in the data set used by the location-determination engine a weight associated with the candidate segment based on, at least in part, the fitness measure.

32. A system for determining a location of a user within an indoor environment, the system comprising:

a receiver adapted to receive (i) radio frequency (RF) signal data from each RF source of a plurality of RF sources, and (ii) tracking data from a sensor; and

a processor adapted to;

(i) compute a yield of the RF source based on the received data, (ii) select a subset of fingerprints from a database of fingerprints, based on, at least in part, the yield associated with each of the plurality of RF sources, and (iii) determine a location of a user based on, at least in part;

(a) the RF signal data received from each of the RF sources in the subset, and (b) the received tracking data.

33. A system for determining a location of a user within an indoor environment, the system comprising:

a receiver adapted to;

(i) receive at a first time instant, a first set of radio frequency (RF) data from a plurality of RF sources, (ii) receive at a second time instant;

(a) a second set of RF data from the plurality of RF sources, and (b) tracking data from an inertial navigation system; and

a processor adapted to compute;

compute based at least in part on the first set of RF data, a first location estimate and a second location estimate;

compute based at least in part on the second set of RF data, a third location estimate and a fourth location estimate;

compute a fifth location estimate based in part on the first location estimate, and a sixth location estimate based in part on the second location estimate, both the fifth and sixth location estimates also being based in part on the tracking data;

compute an error metric by comparing;

(i) the fifth location estimate with at least one of the third and fourth location estimates, and (ii) the sixth location estimate with at least one of the third and fourth location estimates; and

designate based at least in part on the error metric;

(i) one of the first and second location estimates as a location of the user at the first time instant, and (ii) one of the third and fourth location estimates as a location of the user at the second time instant.

34. A system for calibrating a map-point grid for indoor location determination, the grid comprising a plurality of map points, each having a radio frequency (RF) data fingerprint being associated therewith, the system comprising:

a receiver adapted to receive at least one of;

(i) RF signal data from a plurality of RF sources, (ii) a user specified location indication, and (ii) tracking data from a sensor, the tracking data indicating a user'"'"'s movement relative to a base map point; and

a processor adapted to update the map-point grid based on, at least in part, at least one of (i) adjusted RF data, the received RF data being adjusted by the processor using systematic analysis thereof, (ii) the tracking data, and (iii) the location indication.

35. An article of manufacture, comprising a non-transitory machine-readable medium storing instructions that, when executed by a machine configure the machine, for calibrating a map-point grid for indoor location determination, the grid comprising a plurality of map points, each having a radio frequency (RF) data fingerprint being associated therewith, to:

receive at least one of;

(i) RF signal data from a plurality of RF sources, (ii) a user specified location indication, and (ii) tracking data from a sensor, the tracking data indicating a user'"'"'s movement relative to a base map point; and

update the map-point grid based on, at least in part, at least one of (i) adjusted RF data, the received RF data being adjusted using systematic analysis thereof, (ii) the tracking data, and (iii) the location indication.