System and method for occupancy estimation

• US 8,775,359 B2
• Filed: 09/19/2007
• Issued: 07/08/2014
• Est. Priority Date: 09/19/2007
• Status: Expired due to Fees

• Current Assignee: UTC Fire & Security Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Original Assignee: UTC Fire & Security Corporation (Raytheon Technologies Corporation d/b/a RTX)
• Inventors: Tomastik, Robert T.
• Primary Examiner(s): Chen, Alan
• Assistant Examiner(s): BROWN JR, NATHAN H
• Application Number:
  US12/733,752
• Publication Number:
  US 20100250481A1
• Time in Patent Office: 2,484 Days
• Field of Search: 706/52
• US Class Current: 706/52
• CPC Class Codes: G05B 13/04 involving the use of models or simulators
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G06N 7/00 Computing arrangements based on specific mathematical models
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

An occupancy estimator calculates an occupancy estimate (x) of a region based on sensor data (z) provided by one or more sensor devices and a model-based occupancy estimate generated by an occupant traffic model (f). The occupant traffic model (f) is based on predicted movement of occupants throughout a region. The occupancy estimation system includes an occupancy estimator algorithm (20) that combines the sensor data (z) and the model-based occupancy estimate generated by the occupant traffic model (f) to generate an occupancy estimate (x) for the region.

Claims

1. A system for estimating in a region, the system comprising:

an input operably connected to receive sensor data from one or more sensor devices for a current period;

an occupancy estimator operably connected to the input, wherein the occupancy estimator is configured to generate an occupancy, estimate based on an additive combinations of an estimate for the region based on the received sensor data, and

a predictive model-based occupancy estimate generated by an occupant traffic model for a previous period; and

an output operably connected to the occupancy estimator to communicate the occupancy estimate generated by the occupancy estimator;

wherein the occupancy estimator calculates a weighting parameter based, at least in part, on the received sensor data and the occupant traffic model and generates. the occupancy estimate based on the calculated weighting parameter.

2. The system of claim 1, wherein the occupant traffic model generates the occupancy estimate based, at least in part, on a previous occupancy estimate.

3. The system of claim 1, wherein the occupancy estimator generates the occupancy estimates in real-time.

4. The system of claim 1, wherein the occupancy estimate comprises a mean estimate of the number of occupants within the region, an estimate of occupant movement within the region, a probability associated with all possible numbers of occupants associated with the region, a confidence level estimate, a predictive occupancy estimate generated with respect to future points in time, or a combination thereof.

5. The system of claim 1, wherein the occupant traffic model comprises a mathematical model, a computer simulation, a statistical model, or a combination thereof.

6. The system of claim 1, wherein the system comprises a centralized system in which the occupancy estimator is operably connected to receive data from a plurality of sensors located throughout the region and in response generates the occupancy estimate.

7. The system of claim 1, wherein the system comprises a distributed system including a plurality of occupancy estimators, wherein each of the plurality of occupancy estimators receives sensor data associated with a proximate location of the region and executes an algorithm to generate an occupancy estimate for the proximate location based on the received sensor data and an occupant traffic model associated with the proximate location.

8. The system of claim 7, wherein one of the plurality of occupancy estimators is connected to an adjacent occupancy estimators to receive occupancy estimates generated by the adjacent occupancy estimation device with respect to a distal location, wherein the occupancy estimator incorporates the occupancy estimate with respect to the distal location in generating the occupancy estimate for the proximate location.

9. The system of claim 7, wherein one of the plurality of occupancy estimators is connectable to receive sensor data from both a proximate location and a distal location, wherein the occupancy estimators incorporates the sensor data received with respect to the distal location in generating the occupancy estimate for the proximate location.

10. A system for estimating in a region, the system comprising:

an input connected to receive sensor data from one or more sensor devices for a current period;

an occupancy estimator operably connected to the input wherein the occupancy estimator is configured to generate an occupancy estimate based on an additive combinations of an estimate for the region based on the received sensor data, and a predictive model-based occupancy estimate generated by an occupant traffic model for a previous period; and

an output operably connected to the occupancy estimator to communicate the occupancy estimate generated by the occupancy estimator;

wherein

the occupancy estimate comprises a mean estimate of the number of occupants within the region, an estimate of occupant movement within the region a probability associated with all possible numbers of occupants associated with the region, a confidence level estimate, a predictive occupancy estimate generated with respect to future points in time, or a combination thereof wherein the reliability estimate includes a covariance value or a standard deviation value calculated with respect to the region.

11. A system for estimating occupancy in a region the system comprising:

an input operably connected to receive sensor data from one or more sensor devices for a current period;

an occupancy estimator operably connected to the input, wherein the occupancy estimator is configured to generate an occupancy estimate based on an additive combinations of an estimate for the region based on the received sensor data, and

a predictive model-based occupancy estimate generated by an occupant traffic model for a previous period; and

an output operably connected to the occupancy estimator to communicate the occupancy, estimate generated by the occupancy estimator;

wherein

the algorithm employed by the occupancy estimator is comprises an Extended Kalman Filter that generates the occupancy estimate that includes a mean estimate of occupancy for the region and a covariance associated with each mean estimate of occupancy.

12. A method for estimating occupancy in a region, the method comprising:

acquiring sensor data from one or more sensor devices;

calculating a model-based occupancy estimate based on an occupant traffic model that predicts movements of occupants within a region in a future period; and

generating an occupancy estimate for the region based on an additive combination of the acquired sensor data and the model-based occupancy estimate;

wherein

calculating the model-based occupancy estimate includes applying the occupant traffic model to a previous occupancy estimate.

13. The method of claim 12, further including:

calculating a weighting parameter associated with the acquired sensor data and the model-based occupancy estimate; and

generating the occupancy estimate based, in addition, on the calculated weighting parameter.

14. The method of claim 12, wherein the occupancy estimate comprises a mean estimate of the number of occupants within the region, an estimate of occupant movement within the region, a probability associated with all possible numbers of occupants associated with the region, a confidence level estimate, a covariance value, a standard deviation, a predictive occupancy estimate generated with respect to future points in time, or a combination thereof.

15. The method of claim 12, wherein the occupant traffic model comprises a mathematical function, a statistical model, a computer simulation, or a combination.

16. The method of claim 12, wherein generating an occupancy estimate includes:

calculating a measurement prediction based on the model-based occupancy estimate and a sensor model;

calculating an innovation based on a comparison of the measurement prediction to the acquired sensor data; and

applying a weighting parameter to the innovation estimate and combining with the predicted occupancy estimate to generate the occupancy estimate.

17. A distributed system for estimating occupancy within a building, the system comprising:

a first occupancy estimator connectable to receive sensor data associated with a first location and for executing an algorithm to generate a first occupancy estimate for the first location based on an additive combination of the received sensor data associated with the first location and a predictive model-based occupancy estimate generated for the first location b a first occupant traffic model;

a second occupancy estimator connectable to receive sensor data associated with a second location and for executing an algorithm to generate a second occupancy estimate for the second location based on an additive combination of the received sensor data associated with the second location and a predictive model-based occupancy estimate generated for the second located by a second occupant traffic mode; and

a communication network connecting the first occupancy estimator to the second occupancy estimator, wherein the first occupancy estimator communicates the first occupancy estimate to the second occupancy estimator.

18. The distributed system of claim 17, wherein the second occupancy estimator communicates the second occupancy estimate to the first occupancy estimator.

19. The distributed system of claim 17, wherein the first occupancy estimator is connectable to receive sensor data associated with the second location.