Geofence with kalman filter
View Patent ↗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 geofencing, comprising:
a Kalman filter module to filter a plurality of past individual location fixes retrieved for a target device and produce a latest Kalman-filtered location of said target device;
determining a predictive heading and velocity of said target device based on a plurality of Kalman-filtered locations;
detecting a geofence boundary crossing by said target device based on said latest Kalman-filtered location of said target device together with said predictive heading and velocity; and
dynamically determining time intervals between location fixes for said Kalman filter module using said predictive heading and velocity of said target device.
2. The method of geofencing according to claim 1 , further comprising:
detecting a location blunder of said latest Kalman-filtered location using said latest kalman-filtered location.
3. The method of geofencing according to claim 1 , further comprising:
dynamically determining an appropriate number of location fixes for said target device, to determine said latest Kalman-filtered location.
4. The method of geofencing according to claim 1 , further comprising:
dynamically selecting an energy efficient location technique based on said latest Kalman filtered location, and said predictive heading and velocity relative to said geofence.
5. The method of geofencing according to claim 1 , further comprising:
reducing a time interval between location fixes for said target device when said latest Kalman-filtered location is outside a given geofence.
6. The method of geofencing according to claim 1 , further comprising:
establishing a trip profile model for said target device based on historical predictive heading and velocity of said target device.