Geofence with kalman filter

• US 9,113,298 B2
• Filed: 03/04/2013
• Issued: 08/18/2015
• Est. Priority Date: 06/12/2012
• Status: Expired due to Fees

• Current Assignee: TeleCommunication Systems Inc (Comtech Telecommunications Corporation)
• Original Assignee: TeleCommunication Systems Inc (Comtech Telecommunications Corporation)
• Inventors: Qiu, Weigen
• Primary Examiner(s): RAMPURIA, SHARAD K
• Application Number:
  US13/783,719
• Publication Number:
  US 20130331128A1
• Time in Patent Office: 897 Days
• Field of Search: 4554561-457, 4554041-4042, 4554141-4144, 342/357.23, 701/480, 701/510, 701/536
• US Class Current: 1/1
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 5/0268 by deriving positions from different combinations of signals or of estimated positions in a single positioning system
• CPC Class Codes: G01S 5/0294 Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 52/02 Power saving arrangements in wired systems H04L12/12; signaling of mobile application services, e.g. low battery notifications H04W4/20
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

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.

Claims

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.