室內定位已經成為近年來很熱門的議題。大部分這些室內定位的方法仰賴於基礎設施的可用性以及額外的技術培訓，而傳統的行人航位推算(pedestrian dead reckoning, PDR)系統，可以無需額外的設施成本或是技術訓練，即可在移動設備上實驗。然而，由於PDR 系統的誤差會迅速累積，導致系統運行一小段時間就會產生不合格的位置結果。為了解決這個問題，我們提出基於超聲波的合作式行人航位推算 (ultrasound-based collaborative PDR,UCPDR)系統。
UCPDR 系統的主要想法是利用兩個步驟來降低位置座標的估計誤差值：
第一，UCPDR 會運用其他相鄰使用者使用PDR 系統所取得的座標值，以及透過兩人交換超聲波信號所得到的距離資訊，來估算出行人的座標位置。
第二，透過機會式卡爾曼濾波(opportunistic Kalman filter)使用先前的估計位置做為新的測量值來降低估計座標的誤差。除此之外，反向校正(backward correction)系統在UCPDR 系統中被用來改善用戶軌跡的精度。
為了評估UCPDR 的可行性，我們將原型建立於iOS (iPad 操作系統)平台上，而經過多次實驗後，結果顯示，透過鄰居們的位置資訊以及超聲波距離的量測進行位置校正，UCPDR 可以在運行很長一段時間後，定位所造成的誤差仍然可以限制在2 公尺以內 (當步數小於80)。 實驗顯示UCPDR 系統比傳統的
PDR 系統達到更好的定位精度並可實際應用於室內定位。

Indoor localization has become a popular issue in recent years. Most of the indoor localization approaches either require the availability of an infrastructure or the additional training eorts. The traditional pedestrian dead reckoning (PDR) system can be implemented on mobile devices without additional cost and training.
However, it accumulates errors quickly and leads to unacceptable results after a short period of time. To address this issue, we propose the ultrasound-based collaborative pedestrian dead reckoning (UCPDR) system. The main idea of UCPDR is to reduce the estimation error of a pedestrian's location by two steps: First,UCPDR estimates the pedestrian's location based on another nearby pedestrian's location information obtained by PDR and the distance information obtained by ultrasound signals exchanged by the two pedestrians; second, reduce the error estimation through the opportunistic Kalman lter by using the previous location estimation as a new measurement. In addition, the backward correction scheme is used to improve the accuracy of user's trajectory. To evaluate feasibility of UCPDR, a prototype is built on the iOS (iPhone Operating System) platform. The conducted
experiment results show that UCPDR is able to limit the localization error within 2m (when number of steps less than 80) after a long period of time through the help of neighbors location information and ultrasound distance information. Our UCPDR system always achieves a better localization accuracy than the traditional PDR system.

[1] 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.

[2] 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.

[3] M. Youssef and A. Agrawala, "The horus wlan location determination system," in Proc. of ACM MobiSys '05, 2005,
pp. 205~218.

[4] E. Foxlin, "Pedestrian tracking with shoe-mounted inertial sensors," Computer Graphics and Applications, IEEE,
vol. 26, pp. 38~46, Nov.-Dec. 2005.

[5] P. Robertson, M. Angermann, and B. Krach, "Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors," in Proc. of ACM Ubicomp '09, 2009, pp. 93~96.

[6] Y.-T. Li, G. Chen, and M.-T. Sun, “An indoor collaborative pedestrian dead reckoning system," in Proc. of ICPP,
2013, pp. 923~930.
[7] B. Cook, G. Buckberry, I. Scowcroft, J. Mitchell, and T. Allen, “Indoor location using trilateration characteristics,"
in London Communications Symposium, 2005.
[8] V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara, “Accurate gsm indoor localization," in THE PROC. OF
UBICOMP 2005, 2005, pp. 141~158.
[9] 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.
[10] Z. Zhang, D. Chu, X. Chen, and T. Moscibroda, “Sword_ght: Enabling a new class of phone-to-phone ac-
tion games on commodity phones," in Proceedings of the 10th International Conference on Mobile Systems, Ap-
plications, and Services, ser. MobiSys '12. New York, NY, USA: ACM, 2012, pp. 1~14, [Online]. Available:
http://doi.acm.org/10.1145/2307636.2307638.
[11] O. Tekdas and V. Isler, “Sensor placement for triangulation-based localization," Computer Graphics and Applications,
IEEE, vol. 7, no. 3, pp. 681~685, July 2010.
[12] M. Vossiek, L. Wiebking, P. Gulden, J. Wieghardt, C. Ho_mann, and P. Heide, “Wireless local positioning," IEEE
Microwave Magazine, vol. 4, pp. 77~86, December 2003.
[13] A. Savvides, C.-C. Han, and M. B. Strivastava, “Dynamic _ne-grained localization in ad-hoc networks of sensors," in
Proc. ACM MobiCom, July 2001, pp. 166~179.

[14] S.-P. Kuo, B.-J. Wu, W.-C. Peng, and Y.-C. Tseng, “Cluster-enhanced techniques for pattern-matching localization
systems," in IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems, 2007, pp. 1~9.
[15] C. Randell, C. Djiallis, and H. Muller, “Personal position measurement using dead reckoning," in Proc. of IEEE ISWC
'05, October 2005, pp. 166~173.
[16] G. Cai, B. M. Chen, and T. H. Lee, “Coordinate systems and transformations," in Unmanned Rotorcraft Systems
(Advances in Industrial Control). New York: Springer Publishing, 2011, ch. 2, pp. 23~34.
[17] NGIA, “Frame sensor model metadata pro_le supporting precise geopositioning," in NGA NGA.SIG.0002 2.1, 2011.
[18] S. W. Smith, “Moving average _lter," in The Scientist and Engineer's Guide to Digital Signal Process, 2nd, Ed.
California: California Technical Publishing, 1999, ch. 15, pp. 277~284.
[19] D. Gusenbauer, C. Isert, and J. Krsche, “Self-contained indoor positioning on o_-the-shelf mobile devices," in Proc.
of IEEE IPIN '10, September 2010, pp. 1~9.
[20] S. H. Shin and C. G. Park, “Adaptive step length estimation algorithm using low-cost mems inertial sensors," in Proc.
of IEEE SAS '07, February 2007, pp. 1~5.
[21] A. R. Jimnez, 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.
[22] J. Scarlett, “Enhancing the performance of pedometers using a single accelerometer," in AN-900 Application Note.
Analog Devices, Inc., 2007.
[23] G. Welch and G. Bishop, “An introduction to the kalman _lter." University of North Carolina at Chapel Hill Chapel
Hill, NC, USA, Tech. Rep. TR-95-041, 1995, updated: July 24, 2006.
[24] H. Liu, Y. Gan, J. Yang, S. Sidhom, Y. Wang, Y. Chen, and F. Ye, “Push the limit of wi_ based localization for
smartphones," in Proc. of ACM MobiCom '12, 2012, pp. 305~316.
[25] B. Thiel, K. Kloch, and P. Lukowicz, “Sound-based proximity detection with mobile phones," in Proceedings of the
Third International Workshop on Sensing Applications on Mobile Phones, ser. PhoneSense '12. New York, NY, USA:
ACM, 2012, pp. 41~44.
[26] S. Feldmann, K. Kyamakya, A. Zapater, and Z. Lue, “An indoor bluetooth-based positioning system: Concept,
implementation and experimental evaluation," in International Conference on Wireless Networks ICWN '03, June
2003.
[27] I. Constandache, X. Bao, M. Azizyan, and R. R. Choudhury, “Did you see bob?: Human localization using mobile
phones," in Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking. New
York, NY, USA: ACM, 2010, pp. 149~160.

[28] P. G. Stelmachowicz, K. A. Beauchaine, A. Kalberer, and W. Jesteadt, “Normative thresholds in the 8- to 20-khz
range as a function of age," Acoustical Society of America, vol. 86, no. 4, pp. 1384~1391, October 1989.