Geofence with kalman filter
A least squares geofence method that minimizes trigger misfires caused by data variability and location blunders and minimizes delayed/missed entry triggers generated under urban or indoor conditions. The least squares geofence method uses a weighted least squares (LS) model to compute a location estimate for a target device. A LS location estimate is used to determine if a target device is located inside or outside a predefined geofence. The present invention additionally comprises a Kalman filter geofence method that further improves the accuracy of entry/exit geofence triggers. A Kalman filter geofence method uses a Kalman filter to filter location data retrieved for a target device. Filtered location data is used to determine if a target device is located inside or outside a predefined geofence. A Kalman filter geofence method estimates velocity and heading information for a target device to generate accurate entry/exit geofence triggers for devices in fast moving mode.
1. A method of detecting a geofence crossing, comprising:
Kalman filtering a plurality of past individual location fixes retrieved for a wireless device to produce a latest Kalman-filtered location of said wireless device;
determining movement of a target device in a direction away from a geofence boundary;
dynamically determining a more energy efficient location fix before a subsequent Kalman-filtered location of said wireless device, said more energy efficient location fix being determined based on a direction of said wireless device relative to said geofence boundary.
2. The method of detecting a geofence crossing according to claim 1 , wherein said more energy efficient location fix comprising:
a cellular network location fix.
3. The method of detecting a geofence crossing according to claim 1 , wherein said past individual location fixes comprise:
a global positioning satellite (GPS) location fixes.
4. The method of detecting a geofence crossing according to claim 3 , wherein said more energy efficient location fix comprising:
a cellular network location fix.
5. A Kalman filter geofence method, comprising:
obtaining a plurality of locations of a moving target wireless device;
predicting a velocity and heading of said moving target wireless device by filtering said plurality of locations with a Kalman filter module;
triggering a boundary crossing of a moving geofence by said moving target wireless device based on a latest Kalman-filtered location of said moving target wireless device, and said estimated velocity and heading of said moving target wireless device, and based on said moving geofence having a given moving boundary; and
dynamically generating an entry or exit geofence trigger for said target wireless device.
6. The Kalman filter geofence according to claim 5 , wherein:
said target wireless device is operating in a fast moving mode.
7. The Kalman filter geofence according to claim 5 , further comprising:
detecting a location blunder of said latest Kalman-filtered location of said target wireless device using said latest Kalman-filtered location.
8. The Kalman filter geofence according to claim 5 , further comprising:
dynamically determining an appropriate number of individual location fixes retrieved for said target wireless device to produce a subsequent Kalman-filtered location of said target wireless device.
9. The Kalman filter geofence according to claim 5 , further comprising:
obtaining a current location of said target wireless device based both on said latest Kalman-filtered location, and on a predicted velocity and heading of said target wireless device relative to said moving given geofence.
10. The Kalman filter geofence according to claim 5 , wherein:
said moving geofence is moving differently from movement of said moving target wireless device.
11. The Kalman filter geofence according to claim 5 , further comprising:
reducing a time interval between successive location fixes for said target wireless device when said latest Kalman-filtered location is outside said boundary of said moving geofence.
12. The Kalman filter geofence according to claim 5 , further comprising:
creating a trip profile for said target wireless device based on a historical measurement of said predicted velocity and heading of said moving target wireless device.
13. A Kalman filter geofence method, comprising:
obtaining a plurality of locations of a moving target wireless device;
predicting a velocity and heading of said moving target wireless device by filtering said plurality of locations with a Kalman filter module;
triggering a boundary crossing of a moving geofence by said moving target wireless device based on a latest Kalman-filtered location of said moving target wireless device, and said estimated velocity and heading of said moving target wireless device, and based on said moving geofence having a given moving boundary; and
dynamically determining an appropriate number of individual location fixes retrieved for said moving target wireless device to produce a subsequent Kalman-filtered location of said moving target wireless device, based on said predicted velocity of said moving target wireless device.
14. The Kalman filter geofence according to claim 13 , wherein:
said target wireless device is operating in a fast moving mode.
15. The Kalman filter geofence according to claim 13 , further comprising:
detecting a location blunder of said latest Kalman-filtered location of said target wireless device using said latest Kalman-filtered location.
16. The Kalman filter geofence according to claim 13 , further comprising:
dynamically determining an appropriate number of individual location fixes retrieved for said target wireless device to produce a subsequent Kalman-filtered location of said target wireless device.
17. The Kalman filter geofence according to claim 13 , further comprising:
obtaining a current location of said target wireless device based both on said latest Kalman-filtered location, and on a predicted velocity and heading of said target wireless device relative to said moving given geofence.
18. The Kalman filter geofence according to claim 13 , wherein:
said moving geofence is moving differently from movement of said moving target wireless device.
19. The Kalman filter geofence according to claim 13 , further comprising:
reducing a time interval between successive location fixes for said target wireless device when said latest Kalman-filtered location is outside said boundary of said moving geofence.
20. The Kalman filter geofence according to claim 13 , further comprising:
creating a trip profile for said target wireless device based on a historical measurement of said predicted velocity and heading of said moving target wireless device.