Please use this identifier to cite or link to this item:
標題: 應用嵌入式系統與粒子濾波演算法實現無線感測定位之研究
Sensor Network Localization Using Embedded Particle Filtering
作者: 呂孟學
Lue, Mong-Hsueh
關鍵字: Embedded Systems;嵌入式系統;Received Signal Strength Indicator;Particle Filter-based Localization;Ultrasonic Wireless Sensor Networks;粒子濾波演算法(Particle Filter);接收訊號強度指示;超音波;無線感測網路
出版社: 電機工程學系所
引用: [1] Neal Patwari, Alfred O. Hero Matt Perkins, “Relative Location Estimation in Wireless Sensor Networks.” IEEE Transactions on Signal Processing, 51(8), 2137-2148, (2003) [2] Qicai S., Spyros K., Neiyer S. C., Feng N. “Performance Analysis of Relative Location Estimation for Multihop Wireless Sensor networks,” IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 23, NO. 4, 830-838,(2004) [3] Ssu K. F.,Ou C. H., Jiau H. C., “Localization With Mobile Anchor Points in Wireless Sensor Networks,” IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 54, NO. 3, 1187-1197, (2005) [4] 蕭宇成, “分散式自我定位演算法在無線隨意感測網路上之研究,” 中興大學電機系碩士論文, 2008. [5] 黃斐鈺, “嵌入式閘道伺服器在無線感測網路與網際網路資料傳輸之實現,”中興大學電機系碩士論文, 2009. [6] Edgar H. Callaway, “Wireless Sensor Networks Architectures and Protocols,” San Francisco California: Morgan Kaufmann, Elsevier Science, 2004. [7] A. Doucet, N de Freitas and N. Gordon, eds.,“Sequential Monte Carlo Methods in Practice,” Springer-Verlag, 2001 [8] N. J. Gordon, D .J. Salmond and A. F. M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” IEEE Proceedings-F, 140(2): 107-113, April 1993. [9] Doherty, K. Pister, L. El Ghaoui, “Convex Position Estimation in Wireless Sensor Networks,” in Proceedings of IEEE Infocom 2001, April 2001. [10] 陳映熾, “獨立式太陽能無線感測網路即時環境監測系統之研究,”明道大學材料所碩士論文, 2007. [11] TinyOS FROM Available: http:// [12] Texas Instruments, “CC2420 datasheet,” 2006. [13] 瑞帝電通國際有限公司, HBE –Zigbex無線感測網路發展平台教學實驗手冊. [14] 長高科技股份有限公司, XScale270 嵌入式系統實作. 台中: 長高科技股份有限公司, 2007. [15] 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,” 中興大學電機系碩士論文, 2008. [16] Jun Luo., Hersh V. Shukla., and Jean-Pierre Hubaux,“Non-Interactive Location Surveying for Sensor Networks with Mobility– Differentiated TOA,” in Proc. of the 25th IEEE INFOCOM.2006. [17] N. Patwari, et al., “Locating the nodes: cooperative localization in wireless sensor networks, ” IEEE Signal Processing Magazine, vol. 22, pp. 54-69, 2005. [18] Peter Krammer., Herbert Schweinzer,“Locallization of Object Edges in Arbitrary Spatial Positions Based on Ultrasonic Data”in IEEE Sensors Journal, vol 6, NO.1, February 2006
本論文以無線感測網路(Wireless Sensor Networks, WSNs)結合嵌入式系統,發展出可攜型的無線感測傳輸模組,透過嵌入式系統將感測網路收集的資訊經由網路送出至遠端伺服器並載入資料庫,進行後續分析之動作。透過粒子濾波演算法(Particle Filter)與無線感測網路可大量散佈之特性,進行無線感測定位系統之研究。在過去研究接收訊號強度指示(Received Signal Strength Indicator, RSSI)中,發現RSSI受介質影響很大,不易估算距離。因此,本研究以超音波感測器實現室內定位,藉由超音波感測器獲得距離估測之資訊,接著利用粒子濾波演算法實現未知節點之位置估測。系統實測的結果與未知節點真實座標比較後,平均誤差約26公分,顯示此估測系統可行性高。

Due to cost constraints in a sensor node, GPS may not be suitable for the purpose of localization in WSNs. Accordingly, a GPS-free and particle filter-based positioning algorithm is proposed and implemented. Besides the advantages of a Bayesian approach, the particles allow a robust method of location identification, which can be tailored to communicate (virtually) any amount of information between sensors. By quantifying the inherent trade-offs (cost of communication vs. improvement with increased communication), it is likely to lead to an adaptable strategy applicable in a variety of situations. Since the Received Signal Strength Indicator (RSSI) is not reliable due to the influence of the propagation environment, an ultrasonic sensor network localization scheme is proposed. The experimental results show that the particle filter-based localization technique has high fault tolerance level and high feasibility for sensor positioning.
其他識別: U0005-1508201114283300
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM


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