Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9221
標題: 基於歷史資訊之合作式行人定位演算法
A Collaborative Pedestrian Positioning Algorithm Based on the History Information
作者: 何建勳
Ho, Jian-Syun
關鍵字: 無線感測網路;multidimensional scaling(MDS);MDS-MAP定位;定位演算法;WiFi access point map;WiFi positioning system
出版社: 電機工程學系所
引用: [1] 安守中,GPS全球衛星定位系統入門,全華圖書,2003年9月 [2] 美國各主要城市間距離及飛行時間表, http://www.bizvisit.com/zixun/gaikuang-show.asp?id=97 ,2013年6月 [3] J. Kruskal and M. Wish, Multidimensional Scaling. Newbury Park, CA: Sage, 1978. [4] M. Cox and T. Cox, Multidimensional scaling. Handbook of data visual- ization. New York: Springer-Verlag, 2008. [5] I. Borg and P. Groenen, Modern Multidimensional Scaling, Theory and Applications. New York: Springer, 1996. [6] Y. Shang, W. Ruml, Y. Zhang, and M. Fromherz, “Localization frommere connectivity,” presented at the ACM Int. Symp. Mobile Ad Hoc. Netw. Comput., Annapolis, MD, 2003. [7] Y. Shang and W. Ruml, “Improved MDS-based localization,” presented at the IEEE Int. Conf. Comput. Commun., Hong Kong, 2004. [8] X. Ji and H Zha, “Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling,” presented at the IEEE Int. Conf. Comput. Commun., Hong Kong, 2004. [9] Kendall, David G. "A Survey of the Statistical Theory of Shape." Statistical Science. Vol. 4, No. 2, 1989, pp. 87–99. [10] Bookstein, Fred L. Morphometric Tools for Landmark Data. Cambridge, UK: Cambridge University Press, 1991. [11] Seber, G. A. F. Multivariate Observations. Hoboken, NJ: John Wiley & Sons, Inc., 1984. [12] Matlab Procrustes analysis, http://www.mathworks.com/help/stats/procrustes.html , 2013年6月 [13] 孫利民,李建中,陳渝,朱紅松,無線傳感器網絡,北京清華大學,2005年5月。 [14] WIKIPEDIA-Received signal strength indication, http://en.wikipedia.org/wiki/Received_signal_strength_indication, 2012年6月。 [15] WIKIPEDIA-Time of flight, http://en.wikipedia.org/wiki/Time_of_flight, 2012年6月。 [16] WIKIPEDIA-Angle of arrival, http://en.wikipedia.org/wiki/Angle_of_arrival, 2012年6月。 [17] Y. Shang, W. Ruml, Y. Zhang, and M. P. 1. Fromherz, "Localization from mere connectivity," in Proc. of MobiHoc, pp. 201-212, 2003. [18] Jahyoung Koo, Hojung Cha: Unsupervised Locating of WiFi Access Points Using Smartphones. IEEE Transactions on Systems, Man, and Cybernetics, Part C 42(6): PP.1341~1353 2012 [19] Jahyoung Koo, Hojung Cha: Unsupervised Locating of WiFi Access Points Using Smartphones. IEEE Transactions on Systems, Man, and Cybernetics, Part C 42(6): PP.1341~1353 2012 [20] 董依婷,“粒子濾波演算法於無線感測網路分散式定位系統之實 現”,碩士論文,國立中興大學通訊工程研究所,2010年7月。 [21] 黃種瑋,“基於ZigBee之及時室內定位系統開發”,碩士論文,國立中興大學通訊工程研究所,2011年7月。 [22] 顏啟森,“無線感測網路之感測器定位系統實作”,碩士論文,國立中正大學通訊研究所,2005年7月。 [23] Girod, L.,and Estrin, D., “Robust range estimation using acoustic and multimodal sensing,”IEEE Proceedings of Intelligent Robots and Systems (IROS 2001), PP. 1312~1320, Oct 2001。 [24] G. Santinelli, R. Giglietti, A. Moschitta, “Self-Calibrating Indoor Positioning System Based On ZigBeeR devices,”IEEE Instrumentation and Measurement Technology Conference (IMTC 2009), PP.1205~1210 ,May 2009。 [25] Alan Bensky, “Wireless Positioning Technologies and Applications,” Artech House Publishers, January 2008。 [26] Gang Zhou,Tian He, Sudha Krishnamurthy S,et al “Models and Solutions for Radio Irregularity in Wireless Sensor Networks” John A. Stankovic, ACM Transactions on Sensor Net-works 2(2): 221-262 (2006) [27] Girod, L.,and Estrin, D., “Robust range estimation using acoustic and multimodal sensing,”IEEE Proceedings of Intelligent Robots and Systems (IROS 2001), PP. 1312~1320, Oct 2001。 [28] G. Santinelli, R. Giglietti, A. Moschitta, “Self-Calibrating Indoor Positioning System Based On ZigBeeR devices,”IEEE Instrumentation and Measurement Technology Conference (IMTC 2009), PP.1205~1210 ,May 2009。 [29] Alan Bensky, “Wireless Positioning Technologies and Applications”, Artech House Publishers, January 2008。
摘要: 
近年來,無線感測網路的定位偵測被廣泛討論,但是要獲得準確的位置卻還是相當具有挑戰性,無論是在室內或室外,都常面臨電磁干擾、非視距(Non-Line-of-Sight)傳輸等惡劣的無線傳輸通道條件。傳統的定位方式都已經相當成熟,如三角定位、抵達時間差(Time Difference of Arrival,TDOA)等,而定位方式除了傳統定位法外,合作式定位概念也被提出討論,使用者藉由貢獻自身的估測資訊,讓用戶合作定位增加定位精準度。

在本論文中,我們提出新的合作式行人定位演算法,使用經典多維尺度分析(multidimensional scaling,MDS)找出未知節點與已知節點的相對座標、經過普魯克分析(Procrustes analysis,PA)將未知節點與已知節點的相對座標轉換成為絕對座標。

透過無線訊號強度(RSSI)值與經驗模型換算距離,在多維尺度分析中需要找出所有節點間的距離,才可以找出相對位置,而MDS-MAP的方式中利用弗洛依德(Floyd-Warshall) 或代克思托(Dijkstra)最短路徑演算法,解決任意兩點間的最短路徑。最短路徑直接影響MDS相對位置圖形完整度,間接影響普氏分析(Procrustes analysis)的旋轉比對,所以在找所有節點的最短路徑,我們參考歷史節點提出新的演算法MDS-Minimum和MDS-Average。

由模擬結果可以得到,在新的演算法中,有效降低誤差,提高定位精準度。

In recent years, Localization is one of key technologies in wireless sensor networks(WSNs) . the sensor units of the wireless sensor network have made tremendous progress, and it has been used in a wide variety of applications.
Considering the shortages of precision on classic multidimensional scaling anchor-free localization algorithm, new collaborative pedestrian positioning algorithm based on the history information are propose.
we consider the history information and MDS-Minimum、MDS-Average algorithm are modify algorithm of the Floyd.
They calculated the angle based on historical information, speculated that the next distance Dij. MDS-Minimum will take the minimum shortest path, MDS-Average is average path.
Through theoretical analysis and simulations, our simulation results show that MDS-Minimum and MDS-Average algorithm improves the accurancy of node localization significantly.
URI: http://hdl.handle.net/11455/9221
其他識別: U0005-2207201317480600
Appears in Collections:電機工程學系所

Show full item record
 
TAIR Related Article

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.