SYSTEM AND METHOD FOR RFID INDOOR LOCALIZATION

• US 20160139238A1
• Filed: 06/20/2013
• Published: 05/19/2016
• Est. Priority Date: 06/20/2013
• Status: Abandoned Application

• Current Assignee: Qatar University Qstp-B
• Original Assignee: Qatar University Qstp-B
• Inventors: BEKKALI, Abdelmoula, KADRI, Abdullah
• Application Number:
  US14/441,846
• Publication Number:
  US 20160139238A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 13/75 using transponders powered from received waves, e.g. using passive transponders , or using passive reflectors
• CPC Class Codes: G01S 13/878 Combination of several spaced transmitters or receivers of known location for determining the position of a transponder or a reflector G01S13/874 takes precedence
• CPC Class Codes: G01S 5/02525 Gathering the radio frequency fingerprints
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: H01Q 1/007 specially adapted for indoor communication

Abstract

Disclosed is a system for RFID indoor localization for estimating location of a target object in a localization area, comprising: a radio frequency identification (RFID) unit comprising a RFID reader and a plurality of RFID antennas in operative communication with the RFID reader; and a central unit in operative communication with the RFID unit. The central unit is capable of configuring and distributing a plurality of passive reference tags in the localization area and further capable of: collecting data from the passive reference tags through the RFID unit; processing the collected data; and estimating location of the target object. The central unit employs learning-based location estimation by received signal strength RSSI and detection rate fingerprinting of passive reference tags and the use of tags with different backscattered range.

Claims

1. A system for radio frequency identification (RFID) indoor localization for estimating location of a target object in a localization area, comprising:

a radio frequency identification (RFID) unit comprising a RFID reader and a plurality of RFID antennas in operative communication with the RFID reader; and

a central unit in operative communication with the RFID unit, the central unit capable of configuring and distributing a plurality of passive reference tags in the localization area;

wherein the central unit is capable of

collecting data from the passive reference tags through the RFID unit,

processing the collected data, and

estimating location of the target object, and

wherein the central unit employs learning-based location estimation by received signal strength indication (RSSI) and detection rate fingerprinting of passive reference tags and the use of tags with different backscattered range.

2. The system of claim 1, wherein the central unit comprises

a data collection module for configuring and distributing the pre-defined number of passive reference tags in the localization area based on a floor plan and initiating the construction of a radio frequency (RF) map, wherein the data collection module is capable of automatically detecting and collecting backscattered received signal strength indication (RSSI) received by each RFID antenna, detection rate with their associated tag location,

a radio frequency (RF) map design module in operative communication with the data collection module, wherein the RF map design module receives input from the data collection module to characterize the spatio-temporal properties of detection rate and received signal strength (RSS) through training RSS measurements at the passive reference tags with known coordinates to build the RF map,

a database builder module in operative communication with the RF map design module, wherein the database builder module receives input from the RF map design module to store detection rate and RSSI statistical distribution for each passive reference tag, location of passive reference tags, location of RFID antennas and the floor plan of the localization area,

a localization engine module in operative communication with the RF map design module and the database builder module, wherein the localization engine module receives input on the target object from the RF map design module, and wherein the localization engine module receives input on passive reference tags from the database builder module, and

a location estimation module in operative communication with the localization engine module, wherein the location estimation module receives input on all measured location estimates from the localization engine, and wherein the location estimation module is capable of filtering the different location estimates to estimate location of the target object.

3. The system of claim 2, wherein the location of the target object is estimated by obtaining a signal strength and detection rate vector at the target object and identifying the closest matching vector from the RF map.

4. The system of claim 2, wherein the RF map design module comprises

a RSSI statistical sub-module, and

a detection rate statistical sub-module.

5. The system of claim 4, wherein the RSSI statistical sub-module employs a Multivariate Gaussian Distribution and detection rate statistical sub-module employs a Binomial distribution.

6. The system of claim 4, wherein the detection rate statistical module is capable of identifying the detection rate of the passive reference tags by the RFID antenna by estimating the tag response count in a fixed number of interrogation cycles sent from the RFID antenna.

7. The system of claim 2, wherein the database builder module comprises

an RF map database sub-module capable of storing detection rate and RSSI distribution for each passive reference tag, and

a floor map database sub-module capable of storing location of passive reference tags, location of RFID antennas and the floor plan of the localization area.

8. The system of claim 4, wherein the localization engine comprises

a map matching algorithm (MAA) sub-module capable of implementing a map matching algorithm on inputs from the RSSI statistical sub-module in conjunction with the detection rate statistical sub-module, and

a tags backscatter range diversity sub-module capable of reducing the learning area and searching time used by the MAA sub-module.

9. The system of claim 8, wherein the tags backscatter range diversity sub-module is capable processing data from a first passive reference tag with a longer reading range and a second passive reference tag having a lower reading range, wherein the first passive reference tag is capable of localization, and wherein the second passive reference tag is capable of reducing the learning area and the searching time.

10. A method for RFID indoor localization for estimating location of a target object in a localization area, comprising:

configuring a radio frequency identification (RFID) unit and a central unit in operative communication with the RFID unit;

configuring and distributing a plurality of passive reference tags by the central unit;

transmitting data from the passive reference tags to the RFID unit to the central unit;

processing of transmitted data by the central unit; and

estimating location of the target object by the central unit;

wherein the central unit employs learning-based location estimation by received signal strength indication (RSSI) and detection rate fingerprinting of passive reference tags and the use of tags with different backscattered range.

11. The method of claim 10, wherein the RFID unit comprises a RFID reader and a plurality of antennas in operative communication with the RFID reader.

12. The method of claim 10, wherein the central unit comprises

a data collection module for configuring and distributing the pre-defined number of passive reference tags in the localization area based on a floor plan and initiating the construction of a radio frequency (RF) map, wherein the data collection module is capable of automatically detecting and collecting backscattered received signal strength indication (RSSI) received by each RFID antenna, detection rate with their associated tag location,

a radio frequency (RF) map design module in operative communication with the data collection module, wherein the RF map design module receives input from the data collection module to characterize the spatio-temporal properties of detection rate and received signal strength (RSS) through training RSS measurements at the passive reference tags with known coordinates to build the RF map,

a database builder module in operative communication with the RF map design module, wherein the database builder module receives input from the RF map design module to store detection rate and RSSI statistical distribution for each passive reference tag, location of passive reference tags, location of RFID antennas and the floor plan of the localization area,

a localization engine module in operative communication with the RF map design module and the database builder module, wherein the localization engine module receives input on the target object from the RF map design module, and wherein the localization engine module receives input on passive reference tags from the database builder module, and

a location estimation module in operative communication with the localization engine module, wherein the location estimation module receives input on all measured location estimates from the localization engine, and wherein the location estimation module is capable of filtering the different location estimates to estimate location of the target object.

13. The method of claim 12, wherein the location of the target object is estimated by obtaining a signal strength and detection rate vector at the target object and identifying the closest matching vector from the RF map.

14. The method of claim 12, wherein the RF map design module comprises

a RSSI statistical sub-module, and

a detection rate statistical sub-module.

15. The method of claim 14, wherein the RSSI statistical sub-module employs a Multivariate Gaussian Distribution and the detection rate statistical sub-module employs a Binomial distribution.

16. The method of claim 14, wherein the detection rate statistical module is capable of identifying the detection rate of the passive reference tags by the RFID antenna by estimating the tag response count in a fixed number of interrogation cycles sent from the RFID antenna.

17. The method of claim 12, wherein the database builder module comprises

an RF map database sub-module capable of storing detection rate and RSSI distribution for each passive reference tag, and

a floor map database sub-module capable of storing location of passive reference tags, location of RFID antennas and the floor plan of the localization area.

18. The method of claim 14, wherein the localization engine comprises

a map matching algorithm (MAA) sub-module capable of implementing a map matching algorithm on inputs from the RSSI statistical sub-module in conjunction with the detection rate statistical sub-module, and

a tags backscatter range diversity sub-module capable of reducing the learning area and searching time used by the MAA sub-module.

19. The method of claim 18, wherein the tags backscatter range diversity sub-module is capable processing data from a first passive reference tag with a longer reading range and a second passive reference tag having a lower reading range, wherein the first passive reference tag is capable of localization, and wherein the second passive reference tag is capable of reducing the learning area and the searching time.