應用模糊預測方法於感測網路之室內定位系統研究

在現代科技的發達下，很多應用被發展出來了，在這些應用之中定位系統也是發展相當迅速的技術之一。全球定位系統 (Global Position System, GPS)是現今最常見的定位技術，GPS是用衛星訊號來對欲追蹤對象做定位與導航，雖然GPS應用廣泛但由於衛星訊號會被建築物所遮蔽，所以當GPS套用在室內定位時存在定位精度不足的問題，所以必須透過其他定位感測技術來解決室內定位問題。常用的室內定位感測技術主要有紅外線、超聲波和無線電波技術，這些感測技術各有其物理特性與限制。本論文即是針對室內定位系統，以模糊理論為基礎融入位置預測系統，進而提高室內定位的準確度。本研究是應用將ZigBee中無線電波的接收訊號強度轉換成距離，以達到三點定位之方式。由於在室內定位中存在著定位精度不足的問題，除了在測量上本身的誤差之外，non-line of sight (NLOS)也是造成定位精度不足的因素。其是在傳送訊號時遭到阻礙所產生的誤差，本論文就是針對此誤差，以模糊理論為基礎來實作位置預測系統，進而抑制室內定位的非直視性誤差，以達到更精準的定位。本論文所提出的系統主要是透過line of position (LOP )方法計算測量距離以得到移動節點的位置，再將得到位置的座標代入類神經網路進行訓練，進而減少測量時的誤差。另一方面記錄移動節點位置，帶入以模糊理論為基礎，並配合老人行走速度進行運算的位置預測系統，來得到下一時間點移動節點的位置。實驗的模擬結果顯現本研究，所提出的方法，有效地降低測量上與NLOS的誤差。

關鍵字

室內定位；模糊推論；類神經網路；非直視路徑效應；直線定位法

並列摘要

GPS is one of the most common positioning system technologies in locating and navigating object. However, the main disadvantage of GPS is that the satellite signals may be easily blocked by buildings. This condition may result in the low positioning accuracy when the GPS is applied in indoor positioning. For this reason, other positioning technology such as infrared, ultrasonic, or radio technology with its unique physical characteristics has been employed to solve the problems encountered in indoor positioning. This paper focuses on implementing the fuzzy logic system to forecast the location of an object and combining the neural network to improve the accuracy of indoor positioning. In short, the low positioning accuracy in indoor positioning may be caused by instrumental errors, especially the effects by non-line of sight (NLOS). Essentially, NLOS results in errors when the signal is obscured during signal transmission. The purpose of this research is utilizing the forecast location algorithm based on the fuzzy logic system to reduce the NLOS error in indoor positioning. Furthermore, the procedure of this method is designed in the following steps. First, the line of position (LOP) algorithm is used to calculate the position of mobile node. Then, the coordinate of position calculated by LOP is incorporated into neural network to reduce positional errors. In addition to achieve forecast location algorithm, the various walking speeds of an elder is integrated into the fuzzy logic system to estimate the coordinate of mobile node on the next time. The simulation results indicated that the instrumental and NLOS errors were significantly reduced.

並列關鍵字

indoor positioning ； line of position ； fuzzy logic system ； non-line of sight ； Neural Network

參考文獻

[13] ZigBee無線定位開發系統 CC2430/2431使用手冊華亨科技股份有限公司。

[3] J. J. Caffery, “A new approach to the geometry of TOA location,” in Proc. IEEE Vehicular Technology Conf. VTC, vol. 4, Boston. USA ,Sep. 2000, pp. 1943-1949.

[4] X. Wang, Z. Wang, and B. O’Dea, “A TOA-based location algorithm reducing the errors due to non-line of sight (NLOS) propagation,” in Proc. IEEE Vehicular Technology Conf. VTC, Vol. 52, No. 1, Apr. 2001, pp. 97-100.

[5] T.Y. Chen, C.C. Chiu, and T.C. Tu, “Mixing and combining with AOA and TOA for the enhanced accuracy of mobile location,” in Proc. Personal Mobile Communications Conference, Apr. 2003, pp. 276-280.

[6] J.L. Kim, S. Kim, J. Kang, and Y. Kin, “Joint AOA/RSSI based multi-user location system for military mobile base-station,” in Proc. IEEE Military Communications Conf. MILCOM, Nov. 2008, pp. 1-5.

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應用模糊預測方法於感測網路之室內定位系統研究

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