A Hybrid Method for achieving High Accuracy and Efficiency in Object Tracking using Passive RFID

1. A Hybrid Method for achieving High Accuracy and Efficiency in Object Tracking using Passive RFID Lei Yang1, Jiannong Cao1, Weiping Zhu1, and Shaojie Tang2 1Hong Kong Polytechnic University, Hong Kong 2Illinois Institute of Technology, USA

2. Outline • Introduction • Existing Approaches • Our Approach • Evalution • Conclusions

3. Motivation • Object tracking is desired by a lot of applications • Vehicles tracking in the warehouse managment • Wheelchair monitoring and tracking in the elderly healthcare • Passive RFID is suitable in object tracking • Feasibility and low cost of large-scale deployment of passive tags

4. Technical Difficulties • Real-time characterstic • Tracking is more difficult than localization of stationary objects because executiion the algorithm needs to be finished before a deadline • Noisy measurement • RFID reading is noisy, which means the tags have low probability to be detected by the reader even though they are within the object’s reading range • Constrained computional resource on the mobile RFID devices

5. Problem Definition • Given • K tags deployed in the enrionment • The location of the i-th tag Ti • The RFID reader scans frequency f • The reader’s reading range is tr , which can be adjusted • The RFID reading at time k • Assume • The reader has a probability p(r) to read the tags within its reading range • Objective • Estimate the continuous locations of the mobile object using the uncertain RFID readings

6. Existing Approaches • Centroid Localization (CL) [N.Bulusu 2000] • Average the locations of all the tags that have been detected by the RFID reader • The tracking problem is solved by executing CL periodically • Low accuracy in case of low detecting probability • Weighted Centroid Localization (WCL) • Improve the accuracy by assigning weights in averaging the tags’ location • Each tag’s weight equals to the times that the tag has been detected in past N scans • Large error if the object’s speed is large relative to the scanning speed of the RFID reader • Both methods are computationally cheap

7. Estimated Location Estimated Location

8. Existing Approaches • Centroid Localization (CL) [N.Bulusu 00] • Average the location of all the tags that have been detected by the RFID reader • The tracking problem is solved by executing CL periodically • Low accuracy in case of low detecting probability • Weighted Centroid Localization (WCL) [Behnke08] • Improve the accuracy by assigning weights in averaging the tags’ location • Each tag’s weight equals to the times that the tag has been detected in past N scans • Large error if the object’s speed is large relative to the scanning speed of the RFID reader • Both methods are computationally cheap

9. Count the detection times for each tag in recent 5 scans; • The weight of each tags is equal to the count number.

10. t = 1

11. t = 2

12. t = 3

13. t = 4

14. Detection times Estimated Location t = 5 1 4 2 1 3 2

15. v • WCL has large error if the object’s speed is large relative to the scanning speed of the RFID reader .

16. Existing Approaches • Centroid Localization (CL)[N.Bulusu 00] • Average the location of all the tags that have been detected by the RFID reader • The tracking problem is solved by executing CL periodically • Low accuracy in case of low detecting probability • Weighted Centroid Localization (WCL) [Behnke08] • Improve the accuracy by assigning weights in averaging the tags’ location • Each tag’s weight equals to the times that the tag has been detected in past N scans • Large error if the object’s speed is large relative to the scanning speed of the RFID reader • Both methods are computationally cheap

17. Existing Approaches • Particle Filter [D.Hahnel06 ][Schneegans07] ][Vorst08] • The object’s location is calculated by averaging a set of particles • Each particle represents a random location sample, and has a weight in caculating the object’s location • In each iteration the particle evolves through three steps • Prediction - Predict the location of each particle according to its location at previous time and the object’s moving speed • Updating - Update the weight of each particle according to the sensory data • Resampling – Filter out the particles with small weight.

18. Existing Approaches • Particle Filter (PF) • The accuracy is better than CL/WCL, but continuous execution of particle filter suffers from high computational cost • PF achieves high accuracy by sacrificing the computational efficiency

19. Our Approach • Observations • WCL are efficienct but the accuracy is not good when the object’s speed is large • Particle filter has satisfactory accuracy but is costly • It is usual that the object moves with a varying speed • Can we integrate the two approaches together to achieve better efficiency as well as accuracy? • Hybrid Method • Adaptively switch between using WCL and PF according to the estimated velocity of the moving object • When the speed is low, WCL is used; otherwise, PF is used.

20. Our Approach Select the optimal reading range tr Estimate the object’s speed v v < vth v > vth WCL Partical Filter

21. Technical Details • Pratical Issues • How to determine the reading range (or power level) of the reader? • How to estimate the speed of the moving object? • How to determine the threshold for the speed vth?

22. Technical Details • How to adjust the reading range tr (power level) ? • Density of tags is presented by its spacing a • The otpimal a/tr is 0.9 • The necessary tr of the RFID reader should be as large as 1.1a

23. Technical Details • How to Estimate the object’s speed? • Calculate the time duration di(N) that each tag i stays in the reader's reading range in the last N time slots ( ) • Select the maximum one dmax(N) = max {di} to estimate the speed • 2tr is the diameter of the reading range; c is scaling constant depending on the tag density ( 0<c<1 ) • The tag with largest di tends to be the one which is closest to the object's actual moving path

24. Technical Details • Consideration of the threshold vth • Threshold of the speed vth is too low, the hybrid method has little improvement on the computational cost compared with particle filter • If vth is set too high, the hybrid method sacrifices the accuracy too much • We can control the tradeoff between accuracy and efficiency by chosing proper Nth

25. Evaluation • Comparison between WCL and PF • Simulation environment’s size is 4m*4m • The object moves with constant speed along a rectangle trajectory ( a = 0.27m, f=10Hz, tr = 0.3m )

26. Evaluation • How much does the Hybrid Method improve the performance? • The object's velocity varies during the trajectory

27. Evaluation • Indoor wheelchair tracking • RFID tags are densely deployed in a 4m*6m classroom. • The wheelchair moves along a line with a varying speed, which is 0.1 m/s at the first half , 0.6m/s at the second half. • UHF RFID reader with a circularly polarized antenna • The reader has 8 power levels, we tune it on level 12. • tr = 60cm, • a = 50 cm, • f = 10Hz, • Nth = 40

28. Evaluation • Indoor wheelchair tracking • Experimental Results • The hybrid method almost achieve the same accuracy as PF, but outperforms PF a lot in term of computational cost

29. Conclusions • We proposed a hybrid method for achieving high accuracy and efficiency in object tracking • The hybrid method is suitable for tracking the mobile object which moves with a varying speed • Our method is demonstrated to be more computational efficient than PF while guaranteing the same accuracy with PF

30. Thank you!!