近年來，無線網路技術趨近成熟，各種相關應用逐漸提出來，其中又以定位最為受到重視。一般定位技術為GPS，但是GPS因衛星距離遙遠或遮蔽物干擾等因素，無法有效的在室內進行定位。
而在室內定位技術中，又以無線網路的定位方法較符合經濟效益。最早在此環境中所提出的定位方法為RADAR (Radio Detection and Ranging)，其主要概念分為兩階段，第一階段為離線階段(Off-Line)，在該階段中會在環境各個訓練點(Training Node，TN)接收由無線基地台(Access Point，AP)所發出的無線電波訊號強度(RSSI)並儲存至訊號紋資料庫(Fingerprinting Data Base)。第二階段為在線階段(On-Line)，藉由行動裝置接收AP所發出的RSSI跟資料庫進行比對，來估算出使用者的位置。
在本篇論文中，我們以的RADAR方法為基礎下改良。首先我們除了在環境中平均分佈訓練點並測量這些點之外，也會在測量不到的地方增加虛擬訓練點以輔助定位。因RSSI有易受環境干擾的特性，所以我們會先用穩定訊號演算法排除雜訊使RSSI穩定在一個區間範圍內，接著再利用RSSI隨距離衰減的特性，帶入向量(Vector)觀念，首先透過尤拉公式找到訊紋最似點估算出使用者大約位置後。接著在將訊紋最似點到觀察點(Observe)的向量投影到訊紋最似點兩邊的向量上，透過向量的運算求解估算出使用者位於哪個方向，最後推算出使用者最終預估位置。經實驗結果證明，我們演算法可以有效的提升精準度。

In recent years, wireless network technology has reaching maturity. A variety of different application have been proposed gradually. Among these applications, positioning attracted great attention. The traditional positioning technology was GPS, but GPS was vulnerable due to weather changes or shelter interference which cause indoor positioning ineffectively. When it comes to indoor positioning technology, wireless network positioning method is more in line with economic benefits.
In this wireless network conditions, the first proposed positioning method is RADAR (Radio Detection and Ranging), which key concepts can divided into two stages. The first stage is Off-Line. In this stage, various TN (Training Node) was established in wireless network conditions. TN received RSSI (Real-time Signal Strength Indicator) from the AP (Access Point) and stored. Next, Radio Map will be established composed by the TN. The second stage is On-Line, which mobile device receives RSSI from AP and returned to the sever, then compared with Fingerprinting Data Base to estimate the user's location.
In this paper, on the basis of RADAR, we use the characteristic of RSSI that will be attenuate with distance. Moreover, taken into Vector concept. First, we find the Center through the algorithm. Next, we using the vector between Center and Observe, projecting it to Center’s both sides of the vector. Further, the user's estimated position will be obtain through vector operations. The experimental results shows that our algorithm can effectively improve positioning accuracy.

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