現今每個家庭普遍擁有多個遠端控制器，尋找不同的遙控器變成使用者的負擔。為了解決這個問題，我們提出了智慧遙控系統(a smart remote control system, SRCS)。SRCS的主要概念分為兩個步驟，第一步，SRCS透過行人航位推算(pedestrian dead reckoning, PDR)系統建立行人的位置並且量測目標裝置的座標。第二步，SRCS使用這個資訊決定遠端遙控器是否指向該裝置。如果SRCS發現該裝置，則使用介面以及按鈕隨著目標裝置而調整。為了評估SRCS的可行性，我們在Android智慧手機上建立程式並加以實驗。實驗結果顯示當兩個裝置被隔離超過120公分時，SRSC可以有效的減少判斷錯誤。

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 efforts. 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, it reduces the error estimation through the opportunistic Kalman filter 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 results of conducted experiment show that UCPDR is able to limit the localization error within 2m (when number of steps is 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] Herve Rivano Adrien Desportes Alexis Duque, Razvan Stanica. Unleashing the power of led-to-camera communications
for iot devices. In Proc. of IEEE SECON ’16, pages 55–60, 2016.
[2] GSM Association. Gsm association history, 1982. http://www.gsma.com/aboutus/history.
[3] Paramvir Bahl and Venkata N. Padmanabhan. Radar: An in-building rf-based user location and tracking system. In
Proc. of IEEE INFOCOM’00, volume 2, pages 775 – 784, 2000.
[4] Baike. Forward intersection, 2016. http://www.baike.com/wiki/%E5%89%8D%E6%96%B9%E4%BA%A4%E4%BC%9A.
[5] H. Muller C. Randell, C. Djiallis. Personal position measurement using dead reckoning. In Proc. of IEEE ISWC,
pages 166–173, OCT 2005.
[6] Yin Chen, Dimitrios Lymberopoulos, Jie Liu, and Bodhi Priyantha. Fm-based indoor localization. In Proc. of ACM
MobiSys’12, pages 169–182, 2012.
[7] Krishna Chintalapudi, Anand Padmanabha Iyer, and Venkata N. Padmanabhan. Indoor localization without the pain.
In Proc. of ACM MobiCom’10, pages 173–184, 2010.
[8] GSMA. A Guide to Bluetooth Beacons. 2014.
[9] Hong bin Wang Guimin Jia, Xiangjun Wang. Accuracy analysis of space three-line-array photogrammetry based on
forward intersection. In Proc. of ICACTE, pages 391–395, Aug 2010.
[10] Dominik Gusenbauer, Carsten Isert, and Jens Krsche. Self-contained indoor positioning on off-the-shelf mobile devices. In Proc. of IEEE IPIN ’10, pages 1–9, September 2010.
[11] Martin Serror Klaus Wehrle Hanno Wirtz, Jan Ruth. Enabling ubiquitous interaction with smart things. In Proc. of IEEE SECON ’12, pages 256–264, 2015.
[12] Christian Henke. Smartnavi, 2016. https://smartnavi-app.com/home.
[13] Antonio R. Jimnez, Fernando Seco, Carlos Prieto, and J. Guevara. A comparison of pedestrian dead-reckoning
algorithms using a low-cost mems imu. In Proc. of IEEE WISP ’12, pages 37–42, Augest 2009.
[14] Ting-Wei Hou Kuen-Min Lee, Wei-Guang Teng. Point-n-press an intelligent universal remote control system for home
appliances. IEEE Robotics and Automation Society, 13(3):1308–1317, Jul 2016.
[15] Yi-Ting Li, Guaning Chen, and Min-Te Sun. An indoor collaborative pedestrian dead reckoning system. In Proc. of
IEEE ICPP, pages 923–930, 2013.
[16] A. Agrawala M. Youssef. The horus wlan location determination system. In Proc. of ACM MobiSys ’05, pages 205–218, 2005.
[17] Lionel M. Ni, Yunhao Liu, Yiu Cho Lau, and Abhishek P. Patil. Landmarc: indoor location sensing using active rfid.
In Proc. of IEEE PerCom’03, pages 407–415, 2003.
[18] Nokia. Wibree, 2006. http://electronics.howstuffworks.com/wibree.htm.
[19] Ismail Guvenc Nezih Pala Prabath Palathingal, Murat Yuksel. A multi-element vlc architecture for high spatial reuse. In Proc. of ACM VLCS ’15, pages 21–26, 2015.
[20] Jim Scarlett. Enhancing the performance of pedometers using a single accelerometer. In AN-900 Application Note.
Analog Devices, Inc., 2007.
[21] Hendrik Schuster. Android: Compass implementation calculating the azimuth, 2015. https://www.journal.
deviantdev.com/android-compass-azimuth-calculating/.
[22] S. H. Shin and C. G. Park. Adaptive step length estimation algorithm using low-cost mems inertial sensors. In Proc. of IEEE SAS ’07, pages 1–5, February 2007.
[23] Stephen P. Tarzia, Peter A. Dinda, Robert P. Dick, and Gokhan Memik. Indoorlocalization without infrastructure
using the acoustic background spectrum. In Proc. of ACM MobiSys’11, pages 155–168, 2011.
[24] Roy Want, Andy Hopper, Veronica Falco, and Jonathan Gibbons. The active badge location system. ACM Transactions
on Information Systems (TOIS), 10:91–102, Jan 1992.
[25] Andy Ward, Alan Jones, and Andy Hopper. A new location technique for the active office. IEEE Personal Commu-
nications, 4(5):42–47, 1997.
[26] Greg Welch and Gary Bishop. An introduction to the kalman filter. University of North Carolina at Chapel Hill
Chapel Hill, NC, USA, Tech. Rep. TR-95-041, 1995. updated: July 24, 2006.
[27] Wiki. Collision avoidance system, 2016. https://en.wikipedia.org/wiki/Collision_avoidance_system.