由於GPS衛星定位與基地台無線定位系統在室內受到種種因素影響而無法提供精確定位，如何精確定位室內環境位置遂成為研究和應用的熱門議題。為解決無線訊號多重路徑效應對室內定位的影響，本論文提出了離散多重路徑訊號(discrete multipath signal, DMS)模型。空間中每個點接收到的多重路徑訊號不盡相同，利用此特性，將DMS模型做為位置指紋法的特徵，以射線追蹤法(ray tracing, RT)計算自由空間中每個格點的電場強度，建立DMS指紋資料庫，使用倒傳遞類神經網路(back-propagation neural network, BPNN)與廣義回歸類神經網路(generalized regression neural network, GRNN)來估測定位位置，並比較各種影響室內定位結果的因素，例如房間大小、發射源(access point, AP)數量、離線階段資料庫網格大小、類神經網路中隱藏層神經元數目。從實驗結果可得知AP的數量多寡與排列會影響定位結果，模擬實際的小房間場景(10m×8m×4m)，定位誤差約為0.5m，誤差百分比為5%，約有90%的定位誤差在1m以內；大房間場景(100 m×80 m×4 m)約有90%的定位誤差在2m以內。和其他定位方法相比，小房間場景中，RSSI位置指紋法接近DMS定位結果；在大房間場景中，DMS使用BPNN與GRNN的定位結果都較其他方法來得好。

Global positioning system (GPS) is affected by many factors in the indoor environment and cannot provide accurate positioning. How to accurately locate in indoor environments has become very popular in recent years. In order to solve the multipath effects of wireless signal on indoor positioning, this paper proposes a discrete multipath signal model (DMS). Particularly, the multipath signals received at each point in space are not the same. Using this feature, the DMS model is used as a fingerprint for location fingerprinting. The ray tracing is used to calculate the electric field intensity of each grid point in the free space to establish the DMS fingerprint database. Subsequently, the back-propagation neural network (BPNN) and the generalized regression neural network (GRNN) are used to estimate the position. Various factors affecting the indoor positioning are evaluated, such as the size of the room, the number of access points (APs), and the number of hidden neurons in BPNN. The experimental results show that the number and arrangement of APs will significantly affect the results. Simulating a small room scenario (10 m×8 m×4 m), the error percentage is about 5%. Moreover, the distance error for the 90% error probability is within 1 m. In a large room scenario (100 m×80 m×4 m), the distance error for the 90% error probability is 2 m. Compared with conventional positioning methods, the positioning results of the proposed DMS using BPNN and GRNN are better than other methods.

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