行動定位服務（Location-Based Service, LBS）已是現代生活的基本需求之一，雖然GPS在室外具有極佳的定位能力，可惜到了室內後因為訊號遮蔽導致GPS極度不穩定。為了解決室內的定位需求衍生出眾多室內定位技術（如Wi-Fi、藍芽…等），其中依照定位方式可將室內定位技術分為絕對定位與相對定位，絕對定位是根據訊號直接計算位置的離散的定位結果，而相對定位藉由前一個位置與移動狀態推測下一個位置的連續定位結果。
如今在學校、車站等大多公共室內地區都有架設 Wi-Fi 無線存取點（Access Point, AP）供大眾連網使用，本論文藉此提出一套使用環境本身已存在Wi-Fi的絕對定位，搭配智慧型手機計算行人航位推算演算法（Pedestrian Dead Reckoning, PDR）的相對定位而成的室內定位系統。其中Wi-Fi定位使用指紋（Fingerprinting）定位法，並透過主成分分析(Principal Component Analysis, PCA)擷取主要特徵提升長期穩定度；另一方面PDR在短時間的連續定位精準度高也貼近行人移動模式，配合地圖資訊再限縮定位結果維持在合理區域。
本系統具有節省架設新設備成本、適合廣泛應用，將兩種定位特性結合相互補足各自不足處等特點。並且提出一校正演算法藉由Wi-Fi定位向量與PDR定位向量相互判斷新的定位座標的合理性，再依照合理性使用對應的定位校正演算法校正定位座標。特別在當行人被判斷為直行時，透過向量對齊法計算新定位座標，並回授動態偏移角度給PDR面向角獲得更貼近真實的移動方向。
經實驗表明，本論文提出的校正定位演算法在多樓層實驗中平均定位誤差為1.31公尺，相比Wi-Fi定位減少23.8%的定位誤差率，更相對以Wi-Fi切換樓層的原始PDR 減少了77.7%的定位誤差率，有效解決PDR的累積誤差問題也證明其定位精準性；同時定位誤差標準差比起Wi-Fi定位減少了46.2%的定位誤差率，證明所提方法的定位穩定性。

Location-Based Service (LBS) is one of the basic needs in modern society. Although GPS is excellent positioning outdoors, GPS is extremely unstable indoors due to Shadow Effect. To solve the need for indoor positioning, there are plenty of indoor positioning technologies (such as Wi-Fi, Bluetooth, etc.) have been derived. According to the positioning method, indoor positioning technologies can be divided into absolute positioning and relative positioning. Absolute positioning calculates the discrete position using the signal information, while relative positioning utilizes the state of motion and the previous position to infer the following continuous position.
Nowadays, there are Wi-Fi access points (AP) set up for the public to connect to the Internet in most public indoor areas such as schools and stations. This paper proposes an indoor positioning system based on both absolute positioning of Wi-Fi in the environments and relative positioning calculated by the Pedestrian Dead Reckoning (PDR) algorithm in smartphones. Among them, Wi-Fi positioning is computed by fingerprinting positioning method with improve long-term stability by extracting the main features using Principal Component Analysis (PCA); on the other hand, PDR which has high accuracy in continuous positioning in a short time is closer to the state of motion and is further limited by map information to keep positioning results in a reasonable area.
This system has the advantages of saving the cost of setting up new equipment, and wide applications, and combining two positioning characteristics to complement each other's shortcomings. Besides, we propose a rectifying algorithm to detect the rationality of the new position with the Wi-Fi positioning vector and the PDR positioning vector, and then correct the position using the corresponding rectifying algorithm according to the rationality. Especially when the user is detected to move straight, the new position is calculated through the vector alignment method, and the rectified angle is fed back to PDR orientation dynamically to obtain a more realistic moving direction.
Experiments show that the proposed method has a mean positioning error of 1.31 meters in the multi-floor experiment, which is 23.8% less than Wi-Fi positioning, 77.7% less than the original PDR with floors detected by Wi-Fi, solving the problem of accumulated error in PDR, and also proves its positioning accuracy. At the same time, the standard deviation of positioning error of the proposed method reduces by 46.2% compared with Wi-Fi positioning, which proves the positioning stability.

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