室內定位已成為近年來的熱門話題。雖然行人航位推算 (pedestrian dead reckoning, PDR)系統可以方便地實作於具備慣性傳感器的智慧型手機(smartphone) 用於室內定位，但是行人航位推算系統的誤差會迅速累積而導致精確度快速下降而造成推算的位置無法被採用。
為了解決這個問題，我們提出了合作式行人航位推算 (collaborative PDR, CPDR) 系統。CPDR的主要概念是利用使用者鄰居的位置資訊，藉由機會式卡爾曼濾波器 (opportunistic Kalman filter) 重新計算使用者的估算位置，藉以提高其精確度。另一方面，向後校正方法 (backward correction) 用於來更正行人的軌跡的精準度。為了驗證 CPDR 系統，我們在 Apple iPhone 5 建立程式並用以進行實驗。實驗結果顯示CPDR 比 PDR 達到更好的定位精準度並可實際應用於室內定位。

Indoor localization has become a popular topic in recent years. While self-contained pedestrian dead reckoning (PDR) systems can be conveniently implemented on a smartphone with built-in inertial sensors for indoor localization, the error of the estimated position for a PDR system can accumulate quickly and results in an unacceptable position accuracy. To address this issue, we propose the collaborative pedestrian dead reckoning (CPDR) system. The main idea of the CPDR system is when users are near to each other, we can leverage the proximity information to improve their estimated positions by means of the opportunistic Kalman filter. In addition, the backward correction scheme is used to improve the accuracy of user's trajectory. To evaluate the CPDR system, a prototype is implemented on Apple's iPhone 5. The experiment results show that the CPDR system achieves a better position accuracy than the raw PDR system.

[1] R. Bajaj, S. L. Ranaweera, and D. P. Agrawal, “Gps: location-tracking technology,” Computer, vol. 35, pp. 92–94, April 2002.
[2] “Base Transceiver Station(BTS),”http://en.wikipedia.org/wiki/Base transceiver station/.
[3] S.P.Tarzia,P.A.Dinda,R.P.Dick,and G.Memik, “Indoor localization without infrastructure using the acoustic background spectrum,” in Proc. of ACM MobiSys’11, 2011, pp. 155–168.
[4] P. Bahl and V. N. Padmanabhan, “Radar: An in-building rf-based user location and tracking system,” in Proc. of IEEE INFOCOM’00, vol. 2, 2000, pp. 775 – 784.
[5] M. Youssef and A. Agrawala, “The horus wlan location determination system,” in Proc. of ACM MobiSys’05, 2005, pp. 205–218.
[6] E. Foxlin, “Pedestrian tracking with shoe-mounted inertial sensors,” Computer Graphics and Applications, IEEE, vol. 26, pp. 38–46, Nov.-Dec. 2005.
[7] P.Robertson,M.Angermann,andB.Krach,“Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors,” in Proc. of ACM Ubicomp’09, 2009, pp. 93–96.
[8] M. Vossiek, L. Wiebking, P. Gulden, J. Wieghardt, C. Hoffmann, and P. Heide, “Wireless localpositioning,” IEEE Microwave Magazine, vol.4,pp.77–86,December 2003.
[9] K. Chintalapudi, A. P. Iyer, and V. N. Padmanabhan, “Indoor localization without the pain,” in Proc. of ACM MobiCom’10, 2010, pp. 173–184.
[10] Y. Chen, D. Lymberopoulos, J. Liu, and B. Priyantha, “Fm-based indoor localization,” in Proc. of ACM MobiSys’12, 2012, pp. 169–182.
[11] N.B.Priyantha, A.Chakraborty,andH.Balakrishnan, “The cricket location-support system,” in Proc. of ACM MobiCom’00, 2000,pp. 32–43.
[12] A. Ward, A. Jones, and A. Hopper, “A new location technique for the active office,” IEEE Personal Communications, vol. 4, no. 5, pp. 42–47, 1997.
[13] R. Want, A. Hopper, V. Falco, and J. Gibbons, “The active badge location system,” ACM Transactions on Information Systems (TOIS), vol. 10, pp. 91–102, Jan 1992.
[14] L. M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, “Landmarc: indoor location sensing using active rfid,” in Proc. of IEEE PerCom’03, 2003, pp. 407–415.
[15] P.-L. Shih, P.-J. Chiu, Y.-C. Cheng, J.-Y. Lin, and C.-W. Yi, “Energy-aware pedestrian trajectory system,” in Proc. of IEEE ICPPW’11,September 2012,pp.514–523.
[16] H. Wang, H. Lenz, A. Szabo, J. Bamberger, and U. D. Hanebeck, “Wlan-based pedestrian tracking using particle filters and low-cost mems sensors,” in Proc. of IEEE WPNC’11, March 2007, pp. 1–9.
[17] L.KlingbeilandT.Wark, “A wireless sensor network for real-time indoor localisation and motion monitoring,” in Proc. of IEEE IPSN ’08, 2008, pp. 39–50.
[18] P. Stromback, J. Rantakokko, S.-L. Wirkander, M. Alexandersson, K. Fors, I. Skog, and P. Handel, “Foot-mounted inertial navigation and cooperative sensor fusion for indoor positioning,” in Proc. of ION’10, January 2010, pp. 89–98.
[19] C. Randell, C. Djiallis, and H. Muller, “Personal position measurement using dead reckoning,” in Proc. of IEEE ISWC’05, October 2005, pp. 166–173.
[20] D. Gusenbauer, C. Isert, and J. Krsche, “Self-contained indoor positioning on off-the-shelf mobile devices,” in Proc. of IEEE IPIN’10, September 2010, pp. 1–9.
[21] S.H.ShinandC.G.Park, “Adaptive step length estimation algorithm using low-cost mems inertial sensors,” in Proc. of IEEE SAS’07, February 2007, pp. 1–5.
[22] A. Jimenez, F. Seco, C. Prieto, and J. Guevara, “A comparison of pedestrian dead-reckoning algorithms using a low-cost mems imu,” in Proc. of IEEE WISP’12,Augest 2009, pp. 37–42.
[23] J. Scarlett, Enhancing the performance of pedometers using a single accelerometer, 2008.
[24] G.Welch and G.Bishop, “An introduction to the kalman filter,”University of North Carolina at Chapel Hill Chapel Hill, NC, USA, Tech. Rep., 1995.
[25] H. Liu, Y. Gan, J. Yang, S. Sidhom, Y. Wang, Y. Chen, and F. Ye, “Push the limit of wifi based localization for smartphones,” in Proc. of ACM MobiCom’12, 2012,pp. 305–316.
[26] S. Feldmann, K. Kyamakya, A. Zapater, and Z. Lue, “An indoor bluetooth-based positioning system: concept, implementation and experimental evaluation,” in Proc. of ICWN’03, June 2003, pp. 109–113.
[27] I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury, “Did you see bob?: human localization using mobile phones,” in Proc. of ACM MobiCom’10, 2010, pp. 149–160.
[28] C.Peng,G.Shen,Y.Zhang,Y.Li,and K.Tan, “Beepbeep: a high accuracy acoustic ranging system using cots mobile devices,” in Proc. of ACM SenSys ’07, 2007, pp. 1–14.
[29] “MATrix LABoratory(MATLAB),”http://www.mathworks.com/products/matlab/.