Active RFID An Angulation Method for Active RFID Tag using Covariance with Known Tags Toyohisa NAKADA School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST) t-nakada@jaist.ac.jp Hideo ITOH Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST) Hideaki KANAI Center for Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST) Susumu KUNIFUJI School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST) keywords: Active RFID Summary We present a new angulation method for detection of active RFID tags. Angulation method is a method for detecting a position by using angles from fixed base stations to target object of which the method detects the position. When a person cuts across the transmission path from a tag to a reader, the strength of electric wave is generally changed. The method employs fixed tags whose locations are already known. When the strengths of the fixed tag and a target tag are changed concurrently, our method recognizes an angle of the target tag as an angle of the fixed tag. In general an angulation method for electric wave needs array antenas or an antena which rotates on its axis. In contrast, our method needs only some fixed tags whose locations are already known without the needs of changing the other components of position detection system. Therefore, the method can be easily integrated with existing technologies such as RSS, TDOA, and so forth. In this paper we also describe performed preliminary experiment in order to demonstrate an advantage of our method. We could reduce about 17% errors by integrating our method to RSS method. 1 1. 1 [Rekimoto 95] [Weiser 91]
2 Active RFID [Hightower 05] [ 07(1), 07(2)] Butz [Butz 04] Reitmayr [Reitmayr 03] Active RFID Active RFID 3. ID 2 3 1 LAN Proximity,Centroid,Cell-ID 2 Lateration 3 RSS 4 TOA 5 RSS TDOA 6 7 Angulation 2. TOA Air- TDOA Location 1 LAN RSS 2 TOA TDOA RSS Lateration CSL 3 LAN 3 3 NEC 4 x,y,z 3 3 1 2 3 4 http://www.hitachi.co.jp/wirelessinfo/airlocation/ http://pr.fujitsu.com/jp/news/2007/01/9-1.html http://www.placeengine.com/ http://www.nec.co.jp/press/ja/0701/1602.html [Satoh 06] Active RFID [Sashima 03] Hightower [Hightower 02] AOA 3
2 d 2 =f(e 2 ) d 2 =Sqrt((x-x 2 ) 2 +(y-y 2 ) 2 +(z-z 2 ) 2 ) d 1 =f(e 1 ) d 1 =Sqrt((x-x 1 ) 2 +(y-y 1 ) 2 +(z-z 1 ) 2 ) 1 A d x : x f(x): e x : x x x,y x,z x,: x x,y,z: 3 0 d 0 =f(e 0 ) d 0 =Sqrt((x-x 0 ) 2 +(y-y 0 ) 2 +(z-z 0 ) 2 ) Lateration B Particle Filter[ 05] Fingerprinting [Aha 91] 1 5 2 : Particle Filter [Hightower 02, 07(2)] 4 5 RSS 3 2 4 5 TDOA 1m 500 Proximity 4. Angulation Lateration RSS TOA TDOA TDOA AirStation 1 500 RSS Active RFID 5 1 80 TOA TDOA 4 1 Angulation 5 5 http://www.k-ubique.co.jp/active/index.html 4 1 :
8 6 1 IF D >α THEN = 7 T D 1 10 1 1 6 11 1 3 B1 B2 12 7 12 2 12 8 4 3 4 2 Liu [Liu 07] Liu 1 13 9
B1 B2 A1 11 9 1 10 12 13 T b 0 (6) x y z x y z = T b = = X2 X1 Y 2 Y 1 X1 Y 1 t + Z2 Z1 Z1 xa xb xb ya yb t + yb za zb zb (X1 2 + X2 xa X1 (X2 +xa) + Y 1 2 Y 1 Y 2 Y 1 ya + Y 2 ya + Z1 2 Z1 Z2 Z1 za + Z2 za) /(X1 2 2 X1 X2 + X2 2 +Y 1 2 2 Y 1 Y 2 + Y 2 2 (1) (2) +Z1 2 2 Z1 Z2 + Z2 2 ) (3) ds = R dr (4) R = D / (X2 X1) 2 + (Y 2 Y 1) 2 + (Z2 Z1) 2 (5) (xb X1)2 + (yb Y 1) 2 + (zb Z1) 2 D / (X2 X1) 2 + (Y 2 Y 1) 2 + (Z2 Z1) 2 >= (xb xa) 2 + (yb ya) 2 + (zb za) 2 14 P1 P2 2 P2 D A P1 P2 L1 (1) A L1 L1 B A B L2 (2) B L1 (1) L1 t Tb Tb L1 L2 B L1 (3) ds ds L1 P1 dr R (4) R P1 P2 P2 D (5) (7)
13 [ 07(2)] 2 ds dr P1(X1, Y1, Z1) 14 B(xb, yb, zb) P2(X2, Y2, Z2) D A(xa, ya, za) A 2 1 L1 A B P1 P1 P2 2 A B DGPS 1 B Tb (6) 2 A B [Liu 07] (5) (4) ds 4 2 (5) R (4) dr P1 B (7) 2 (6) (7) 4 5 RSS RF Code Active RFID Spider 6 6 http://www.rfcode.com/ 4 4 GPS DGPS DGPS
15 16 28 2600 25 2000 A C B D Lateration 1 RSS TDOA LAN R2 17 R1 C 5. 6. RSS RSS 2 15 6 1 RSS 5000mm 1500mm RSS RSS 16 17 725.86mm 602.92mm 120mm 17%
R1t2 R1t2 R2t1 2004. [Hightower 02] Jeffrey Hightower, Barry Brumitt and Gaetano Borriello: The Location Stack: A Layered Model for Location in Ubiquitous Computing, 4th IEEE Workshop R1 on Mobile Computing Systems & Applications (WMCSA 2002), pp.22 28, 2002 [Hightower 05] Jeffrey Hightower, Sunny Consolvo, Anthony t2 LaMarca, Ian Smith and Jeff Hughes: Learning and Recognizing the Places We Go, In Proceedings of Ubicomp 2005, t1 Tokyo, Japan. September 2005. [ 05] :, Vol.88, No.12, pp.989-994, 2005. [Liu 07] Y. Liu, L. Chen, J. Pei, Q. Chen, Y. Zhao: Mining R2 Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays, Proceedings of the Fifth IEEE International Conference on Pervasive Computing 18 and Communications (PerCom2007), pp.37-46, 2007. 1 2 [ 07(1)],, : 1 t1 t2,, Vol. 48 No. 1, pp.148-162, Jan. 2007. [ 07(2)],, :,, Vol.48 6 2 No.12, pp.3962-3976, Dec. 2007. [Reitmayr 03] G. Reitmayr and D. Schmalstieg: Location based applications for mobile augmented reality, 4th Australasian User Interface Conference, pp.65 73, 2003. 1 1 [Rekimoto 95] Jun Rekimoto and Katashi Nagao: The 1 World through the Computer: Computer Augmented Interaction with Real World Environments, Proceedings of UIST 95, pp.29-36, 1995. [Satoh 06] Ichiro Satoh: Location-based Services in Ubiquitous Computing Environments, International Journal of Digital Libraries, vol.6, no.3, pp.280-291, Springer, 2006. [Sashima 03] Akio Sashima, Koichi Kurumatani, and Noriaki Izumi: Location-mediated service coordination in ubiquitous computing, In Proc. of the Workshop on Ontolo- 7. gies in Agent Systems, 2nd International Joint Conference on Autonomous Agents and Multi-Agent Systems, pages http://ceur-ws.org/vol-73/. Deutsche Bibliothek, 2003. [Weiser 91] Mark Weiser: The computer for the 21th century, Scientific American. September 1991. RSS TDOA 17% [Aha 91] D. Aha, D. Kibler: Instance-based learning algorithms, Machine Learning, 6:37-66, 1991. [Butz 04] A. Butz, M. Schneider and M. Spassova: Search- Light - A Lightweight Search Function for Pervasive Environments, Pervasive Computing Proceedings, pp.351 356,