Location and tracking system, method and device using wireless technology

• US 8,179,253 B2
• Filed: 09/21/2009
• Issued: 05/15/2012
• Est. Priority Date: 01/19/2006
• Status: Expired due to Fees

• Current Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Original Assignee: Board of Regents of the University of Texas System (University of Texas System)
• Inventors: Zaruba, Gergely V., Huber, Manfred, Levine, David, Kamangar, Farhad A.
• Primary Examiner(s): PHAM, TOAN NGOC
• Application Number:
  US12/563,598
• Publication Number:
  US 20100134276A1
• Time in Patent Office: 967 Days
• Field of Search: 340/572.1, 340/539.13, 340/539.21, 340/539.23, 340/573.4, 340/6.1, 340/8.1
• US Class Current: 340/539.13
• CPC Class Codes: G01S 5/0258 by combining or switching between measurements derived from different systems
• CPC Class Codes: G01S 5/14 Determining absolute distances from a plurality of spaced points of known location
• CPC Class Codes: G07C 9/28 the pass enabling tracking or indicating presence
• CPC Class Codes: G07C 9/29 the pass containing active electronic elements, e.g. smartcards

Abstract

A system, method, and devices for locating an object, in which the system includes objects having location tags for projecting data about the object, a processing unit for receiving data about the object, and an algorithm for processing the data. Typically, the location tag includes at least one mobility sensor that projects sensory data about the object, a wireless transceiver that projects received signal strength indication data about the object and a microprocessor. The processing unit receiving the sensory data and the received signal strength indication data about the object. The algorithm processes the data, provides a location estimate about the object and thereby locates the object.

Claims

1. A system for locating objects, comprising:

an object having a location tag, the location tag comprising;

a mobility sensor to detect physical movement of the object and to provide sensory information pertaining to movement of the object;

a wireless transceiver unit to provide received signal strength indication (RSSI) information;

a processing unit coupled to the mobility sensor and to the wireless transceiver unit, the processing unit to cause the wireless transceiver to transmit data about the object, the data including the sensory information and the RSSI information; and

at least one algorithm for processing the sensory information and the RSSI information to calculate a location estimate about the object and thereby locate the object, wherein calculating the location estimate is based at least in part on a mobility model and a measurement model.

2. The system of claim 1, wherein the system further comprises a wireless communications system or network.

3. The system of claim 2, wherein the wireless communications system comprises a wireless transceiver and a wireless local area network, a wireless metropolitan area network, or a wireless personal area network.

4. The system of claim 1, wherein the mobility sensor is an accelerometer, an angular rate sensor, a gyroscope, or a gravimeter.

5. The system of claim 1, wherein the processing unit is a microprocessor, a microcontroller, a processor, a single-chip microcomputer, or an embedded computer.

6. The system of claim 1, wherein the location tag is associated with a node at a fixed location.

7. The system of claim 1, wherein the location tag is associated with a central node at a fixed location.

8. The system of claim 1, wherein the algorithm, when executed by the location tag, to perform operations comprising:

creating the mobility model for the object, based at least in part on the sensory information from the object pertaining to movement of the object;

creating the measurement model for the object, based at least in part on the RSSI information; and

calculating the location estimate for the object, based at least in part on the mobility model and the measurement model.

9. The system of claim 8, wherein the algorithm, when executed by the location tag, to perform operations further comprising:

wherein the operation of creating the measurement model comprises creating a probability distribution of estimated locations for the object at particular points in time, based at least in part on the RSSI information;

wherein creation of the probability distribution involves (a) calculating probabilities for particles based on current states of the particles, (b) using the calculated probability for each particle to weigh said particle, (c) creating a cumulative distribution of new weights of all particles, and (d) using the cumulative distribution to resample each particle so the particles have uniform weights; and

wherein the operation of resampling each particle so the particles have uniform weights comprises potentially moving a particle from a less likely estimated location to a more likely estimated location.

10. The system of claim 9, wherein the operation of calculating the location estimate for the object, comprises:

displacing the resampled particles, based on the mobility model; and

after displacing the resampled particles, selecting a displaced particle as the location estimate for the object, based at least in part on probabilities associated with the displaced particles.

11. A method for locating an object, comprising:

obtaining received signal strength indication (RSSI) information from the object, wherein the RSSI information is transmitted from a wireless transceiver unit associated with the object;

obtaining sensory information pertaining to movement of the object, wherein the sensory information is transmitted from a mobility sensor associated with the object;

receiving the sensory information and RSSI information about the object at a processing unit;

processing the sensory information and RSSI information about the object in at least one algorithm that provides a location estimate about the object and thereby locates the object, wherein providing the location estimate is based at least in part on a mobility model and a measurement model.

12. The method of claim 11, wherein the sensory information is obtained from a device capable of detecting a change in motion of the object as a linear or rotational value.

13. The method of claim 11, wherein the processing unit is a microcontroller.

14. The method of claim 11, wherein the RSSI information about the object and sensory information about the object are transmitted as raw data to the processing unit.

15. The method of claim 11, wherein the RSSI information is obtained from a wireless device that transmits electromagnetic signals.

16. The method of claim 11, wherein the algorithm samples RSSI information and combines it with cumulative sensory information about object movement to estimate displacement and location of the object.

17. The method of claim 11, further comprising:

creating the mobility model for the object, based at least in part on the sensory information from the object pertaining to movement of the object;

creating the measurement model for the object, based at least in part on the RSSI information; and

calculating the location estimate for the object, based at least in part on the mobility model and the measurement model.

18. The method of claim 17, further comprising:

wherein the operation of creating the measurement model comprises creating a probability distribution of estimated locations for the object at particular points in time, based at least in part on the RSSI information;

wherein creation of the probability distribution involves (a) calculating probabilities for particles based on current states of the particles, (b) using the calculated probability for each particle to weigh said particle, (c) creating a cumulative distribution of new weights of all particles, and (d) using the cumulative distribution to resample each particle so the particles have uniform weights; and

wherein the operation of resampling each particle so the particles have uniform weights comprises potentially moving a particle from a less likely estimated location to a more likely estimated location.

19. A method to calibrate a set of mobility sensors for locating an object, comprising:

sampling raw sensory information from each mobility sensor periodically;

applying a de-warp matrix to the raw sensory information to determine mobility sensor output, wherein the mobility sensor output is modified to provide readings in a normalized ego-centric mobility coordinate system;

obtaining displacement and orientation information of the object from mobility sensors output; and

calculating mobility model based at least in part on displacement and orientation information of the object.

20. The method of claim 19, wherein an offline algorithm is used to derive the de-warp matrix that can be applied to the raw sensory information.

21. A method, comprising:

compensating for the gravitational force of the earth in mobility sensor readings to locate an object, comprising;

sampling raw sensory information from each mobility sensor periodically;

filtering out gravitational force components from the raw sensory information to determine mobility sensor output;

obtaining displacement and orientation information of the object from mobility sensors output; and

calculating mobility model based at least in part on displacement and orientation information of the object.