使用逆矩陣計算加權幾何精度因子（weighted geometric dilution of precision, WGDOP）的方法已廣泛應用於通信定位。但由於逆矩陣的複雜計算，求出加權幾何精度因子是耗時的。所以本論文提出藉由人工類神經網路（artificial neural network, ANN）模仿生物神經連接的計算能力來尋找最佳近似解。在本文中，我們提出用萊文貝格－馬夸特演算法(Levenberg-Marquardt algorithm, LMA)去訓練類神經網路，並使我們的輸出值近似加權幾何精度因子值。透過加權幾何精度因子選擇最小加權幾何精度因子值的基地台集來定位移動台位置，可以減少幾何分佈的影響，提高定位精度。
本文中，我們在蜂窩無線通信系統中，選擇幾何精度因子值最小的四個基地台，並使用無線電波的抵達時間(time of arrival, TOA)生成四個測量圓，再次使用萊文貝格－馬夸特演算法訓練圓形交叉點來估測行動設備的位置，藉由上述方法降低計算複雜性和定位誤差。根據模擬分析結果，本論文所提出先經過萊文貝格－馬夸特演算法近似加權幾何精度因子，藉此挑選基地台集，再藉由萊文貝格－馬夸特演算法訓練定位結果，證實可提供擁有最佳定位精確度且更有效率之行動台位置估測。並且我們在本研究中提出的架構可以應用於全球定位系統、無線感測網路和移動通信系統中。

The methods on using inverse matrix to calculate weighted geometric dilution of precision (WGDOP) have been widely used on communications positioning. Due to the complicated calculation of the inverse matrix, searching the optimal solution is time-consuming. Therefore, this thesis proposes searching the optimal solution by the artificial neural network (ANN) utilizing the computing ability of counterfeiting biological neural connection. In this thesis, we propose and use the program that can approximate the value of WGDOP by neural network with Levenberg-Marquardt algorithm (LMA). By selecting the base station set with the minimum of WGDOP to locate the mobile station position, it can reduce the effects of geometric distribution and improve positioning accuracy.
In this thesis, we select the four base stations with minimum of WGDOP, and use time of arrival (TOA) to generate four measuring circles in cellular wireless communication system, and then estimate the position of MS by training circular intersection points with Levenberg-Marquardt algorithm. It will reduce the computational complexity and the location error. According to simulation and analysis result, this thesis firstly uses Levenberg-Marquardt algorithm to approximate the WGDOP, and then select base station set. Finally, train and get the positioning result by the Levenberg-Marquardt algorithm. It is confirmed having more accurate positioning accuracy and more efficient mobile station position estimation. In this thesis, the architecture we proposed in this research can be applied to the global positioning system, wireless sensing network and mobile communication system.

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