METHOD OF SENSOR DATA FUSION FOR PHYSICAL SECURITY SYSTEMS

• US 20100134285A1
• Filed: 12/02/2008
• Published: 06/03/2010
• Est. Priority Date: 12/02/2008
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Holmquist, Kurt
• Application Number:
  US12/326,149
• Publication Number:
  US 20100134285A1
• Time in Patent Office: Days
• US Class Current: 340/541
• CPC Class Codes: G08B 13/2494 by interference with electro-magnetic field distribution combined with other electrical sensor means, e.g. microwave detectors combined with other sensor means
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

A method of sensor data fusion in a physical security system of a monitored area is provided. The method comprises receiving data inputs from one or more sensors in the physical security system, with the monitored area being overlaid with a grid defining a plurality of locations. The method further comprise selecting one or more potential paths of one or more potential intruders through the monitored area using an iterative process, which takes into consideration a sequence of sensor inputs and assumed limitations on the mobility of the one or more potential intruders. A confidence metric is then produced for each selected potential path.

Claims

1. A method of sensor data fusion, the method comprising:

receiving data inputs from one or more sensors in a physical security system of a monitored area overlaid with a grid defining a plurality of locations;

selecting one or more potential paths of one or more potential intruders through the monitored area using an iterative process, taking into consideration a sequence of sensor inputs and assumed limitations on mobility of the one or more potential intruders; and

producing a confidence metric for each selected potential path.

2. The method of claim 1, wherein the security system has a finite set of discrete states that each correspond to a different grid location, and the one or more potential paths at any iteration comprise a sequence of time-stamped states.

3. The method of claim 1, further comprising triggering an alarm condition when the confidence metric exceeds a predetermined threshold.

4. The method of claim 1, further comprising displaying one or more paths in which the confidence metric exceeds a predetermined threshold to allow an operator to assess a situation.

5. The method of claim 1 wherein, for each of the one or more sensors providing a binary output, the method incorporates:

a likelihood metric for a sensor being inactive when there is an intruder at a grid location;

or

a likelihood metric for a sensor being active when there is an intruder at a grid location.

6. The method of claim 1, wherein the one or more sensors comprise direct contact sensors, or remote sensing sensors.

7. The method of claim 1, wherein the one or more sensors comprise switches, magnetically activated relays, electric eyes, passive infrared sensors, or microwave proximity sensors.

8. The method of claim 1, wherein the one or more sensors comprise target location estimating sensors.

9. The method of claim 1, wherein the one or more sensors comprise radar, light detection and ranging sensors, video analytics, or seismic sensor arrays.

20. A computer program product, comprising:

a computer readable medium having instructions stored thereon for a method of sensor data fusion in a physical security system according to claim 1O.

10. A method of sensor data fusion in a physical security system for a monitored area, the method comprising:

(a) receiving data inputs from one or more sensors in the physical security system, wherein the monitored area is overlaid with a grid defining a plurality of locations;

(b) determining an elapsed time from a difference between a time reference for a current iteration and a time reference used for a previous iteration in response to the inputs from the one or more sensors;

(c) determining a total number of locations kept in one or more retained paths based on the elapsed time, including the locations added to one or more prefix paths in the current iteration;

(d) initializing an index for iteration over all state values used for a current location;

(e) computing a target-is-at-state metric at the current location;

(f) accumulating a target-is-at-state metric into a current-sensor-indication metric for all locations in the grid;

(g) repeating steps (e) and (f) if the number of the current iteration is less than a total number of locations in the grid;

(h) replacing the current-sensor-indication metric with an arithmetic mean;

(i) initializing an index for iteration over all state values used for the current location;

(j) generating and evaluating all possible new paths to the current location;

(k) storing a new set of paths that have been retained for the current location;

(l) repeating steps (j) and (k) if the number of the current iteration is less than the total number of locations in the grid;

(m) initializing an index for iteration over all state values used for the current location;

(n) normalizing one or more metrics for all retained paths for the current location;

(o) replacing the prefix paths with the new paths in the current iteration;

(p) repeating steps (n) and (o) if the number of the current iteration is less than the total number of locations in the grid;

(q) evaluating the metrics for all retained paths to determine if an alarm condition exists; and

(r) storing the time reference used for the current iteration.

11. The method of claim 10, wherein computing the target-is-at-state metric in step (e) further comprises:

(i) initializing the target-is-at-state metric to be one;

(ii) initializing an index for iteration over all of the one or more sensors in the physical security system;

(iii) if a sensor provides a target location, determining whether a new target detection report is currently available;

(iv) if a new target detection report is currently available;

deriving a reported-target-location metric;

accumulating the target-is-at-state metric based on a multiplication of the reported-target-location metric with the current target-is-at-state metric; and

skipping to step (x);

(v) if a new target detection report is not currently available, skipping to step (x);

(vi) if the sensor does not provide a target location, determining whether the sensor is active;

(vii) if the sensor is active, deriving a binary-sensor-state metric of likelihood that the sensor is active given that there is a target at the current location;

(viii) if the sensor is inactive, deriving a binary-sensor-state metric of likelihood that the sensor is inactive given that there is a target at the current location;

(ix) accumulating the target-is-at-state metric based on a multiplication of the binary-sensor-state-metric with the current target-is-at-state metric;

(x) if the iteration number is less than the total number of sensors, repeating steps (iii) to (ix).

12. The method of claim 10, wherein generating and evaluating all possible new paths in step (j) further comprises:

(i) creating a new set of retained paths for each location after each iteration;

(ii) initializing an iteration index for a previous location on a potential path;

(iii) initializing an index for iteration over all state values as used for the previous location;

(iv) deriving a set of new paths for using the retained paths for the previous location as the prefix path with added extension from the previous location to the current location;

(v) if the iteration number used for the previous location is less than the total number of locations in the grid, repeating step (iv);

(vi) initializing an index for iteration over all paths in the new set of retained paths;

(vii) adjusting the path metric for each path in the new set of retained paths ending at the current location to account for the set of likelihood metrics for the observed set of sensor indications under the assumption that the target is at the current location;

(viii) adjusting the state-entry-metric for a new path node for the current location for each path in the new set of retained paths to account for the set of likelihood metrics for the observed set of sensor indications under the assumption that the target is at the current location; and

(ix) if the iteration number of the new set of retained paths is less than the total number of paths retained for each location in the grid, repeating steps (vii) and (viii).

13. The method of claim 10, wherein normalizing one or more metrics for all retained paths in step (n) comprises:

(i) initializing an index for iteration over all paths in the new set of retained paths for the current location;

(ii) normalizing the path metric for each path in the new set of retained paths by dividing by a metric indicating the likelihood of occurrence of the current set of sensor indications with no assumptions regarding target location;

(iii) normalizing the path metric for each path in the new set of retained paths by dividing by a metric indicating the likelihood of occurrence of the current set of sensor indications with no assumptions regarding target location; and

(iv) if the iteration number is less than the total number of paths retained for each location in the grid, repeating steps (ii) and (iii).

14. The method of claim 12, wherein deriving a set of new paths in step (iv) comprises:

determining the required average velocity in each of an x-coordinate and a y-coordinate for a target that has moved from a previous location to the current location in the time that has elapsed since the last iteration;

if the required velocity is consistent with assumptions about the mobility of the target, obtaining a metric indicating the likelihood that a target has moved from a previous location to the current location in the last time interval;

initializing an index for iteration over the new paths;

creating and evaluating a new path using paths retained by the previous location as prefix paths and extended by a transition from the previous location to the current location;

if the iteration number is less than the total number of paths retained for each location in the grid, repeating the previous step;

15. The method of claim 14, wherein creating and evaluating a new path comprises:

calculating a prefix path metric;

calculating a new path evaluation metric for comparing the new paths all ending in the current location to determine which paths are retained for subsequent iterations;

determining whether all elements in the new path set are assigned to a new path;

if all elements in the new path set are assigned to a new path;

creating a new path to the current location using one of the paths retained by the previous location as the prefix path;

assigning the newly created path to the next free element in the new path set; and

incrementing the index that tracks unassigned locations in the new path set; and

if all elements in the new path set are not assigned to a new path;

finding the index of the path in the new path set having the weakest metric;

if the metric of the newly created path is not less than the weakest metric currently in the new path set, creating a new path to the current location using one of the paths retained by the previous location as the prefix path; and

replacing the path in the new path set having the weakest metric with the newly created path.

16. The method of claim 15, wherein creating a new path to the current location comprises:

creating a new path structure;

setting a new path metric based on the new path evaluation metric previously calculated;

setting a state index member of a first path node in the new path to the current state index.

setting a state transition metric member of the first path node in the new path to a previously calculated state transition metric.

initializing an index to iterate over the path nodes in the new path that precedes the newest path node in time;

copying a state index from a corresponding path node in the prefix path;

copying a state entry metric from a corresponding path node in the prefix path;

if the iteration number for the path nodes is less than the total number of paths retained for each location in the grid, repeating the immediately preceding step.

17. The method of claim 15, wherein finding the index of the path in the new path set having the weakest metric comprises

a) initializing a weakest path index to one;

b) if more than one path is being retained for each location, initializing the weakest path index to iterate over the paths in the new path set after the first element;

c) if the path at the index of the current iteration does not have a weaker metric than the path currently identified by the weakest path index;

continue checking the metric of the next path; and

setting the index of the weakest path to the index of the current iteration;

d) if the iteration number for the new path is less than the total number of paths retained for each location in the grid, repeating step c).

18. The method of claim 15, wherein calculating the prefix path metric comprises:

a) initializing the path metric to be computed for accumulation via multiplication;

b) initializing an index for iteration over the path nodes in the path;

c) if time weighting is not being used, accumulating a path metric using the path node state entry metric value directly;

d) if time weighting is being used;

computing a weighted value for the state entry metric stored for the path node at the index of the current iteration; and

accumulating the time-weighted value of the state entry metric;

e) if the iteration number over the path nodes is less than the total number of paths retained for each location in the grid, incrementing the index for iteration over the path nodes and repeating the method starting at step c).

19. The method of claim 18, wherein computing the weighted value for the state entry metric comprises:

computing a time-weighted value of the path node state entry metric by taking the path node state entry metric to a power that decreases for increasing values of the index for iteration over the path nodes.