於全球衛星導航系統中，幾何精度因子作為挑選高定位精度衛星組合之標準，因此我們引用幾何精度因子之觀念，並推導其加權形式用於挑選具不同雜訊變異數之基地台組合。透過所有可能基地台組合之加權幾何精度因子計算，我們將採用具最小值之加權幾何精度因子的基地台組合作為定位之基地台組合。於定位行動台之階段，我們運用倒傳遞神經網路之架構建立非線性回歸之關係。透過訊號抵達時間或訊號接收強度，估算行動台與最佳基地台組合之量測距離，且藉由各基地台之座標產生量測圓。網路模型之主要輸入資訊為各量測圓交集區域之各交點座標，而輸出為行動台之座標位置，並使用適應性動差估計演算法優化網路模型之權重與偏量值。
本文採用四個基地台為一組合進行行動台之定位，經由計算所有可能基地台組合之加權幾何精度因子，挑選最佳定位基地台組合。我們以神經網路模型作為定位架構，由已知資料集之訓練，以期得到高精準度之行動台估測模型。於模擬結果中，我們使用了三種不同的誤差模型模擬實際的非視距傳播誤差，而模擬環境為蜂巢式網路系統。我們驗證了透過加權幾何精度因子挑選基地台組合之定位效能，也比較了其他定位演算法與本文提出的定位架構之定位精準度。

In the global navigation satellite system (GNSS), the selection criterion of satellite subsets with high precision is based on geometric dilution of precision (GDOP). We introduce the concept of GDOP and derive the weighted form for the base stations with different noise variances. With the weighted GDOP (WGDOP) calculation of all the possible subsets, we choose the base station subset with the minimum of WGDOP value as the positioning set. In the phase of positioning the mobile station, we utilize the architecture of back-propagation neural network to construct the nonlinear regression relationship.
The distance from the mobile to the base stations in the optimal base station subset can be determined by the time of arrival or received signal strength. The measured circles are produced by the locations of the base stations and the measurement distance from the base stations to the mobile station. The main inputs of the network model are the coordinates of the intersection points in the overlap of the measuring circles. The outputs are the mobile station location of the estimation. We apply the adaptive moment estimation (Adam) algorithm to optimize the model of weights and biases.
In the thesis, we employ the four base stations as a subset and compute the WGDOP value of all the available subsets to select the best base station subset. We establish the positioning architecture based on neural network. We expect, through the training by the known dataset, to achieve the model of mobile station estimation with high precision. With the simulation environment set as cellular network system, we imitate the actual non-line-of-sight propagation error by three different noise models. We verify the positioning effectiveness of selecting the base stations set with the WGDOP calculation. We also compare the location accuracy of the proposed positioning scheme with other algorithms.

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