近年來，由於通訊系統的蓬勃發展，感測器網路(Sensor Network)隨之成為近幾年來甚為熱門的研究話題，而在其相關的研究當中，有關感測器位置的估測為一基礎且重要的一個環節。因此，有許多不同的定位演算法被相繼發展出來。然而，大多數的演算法需要有已知座標的節點存在，而這些座標資訊，多需藉由全球衛星定位系統(GPS)予以提供。因此，若當標的環境位於室內或是具有環境物的干擾，乃至於氣候的影響，都可能造成定位作業發生障礙。故之無已知座標節點感測器存在的定位演算法亦漸受相關研究人員的重視。
由於在大多數的情形之下，感測器網路之間僅需知道彼此之間的相關位置，即可進行網路的相關應用。因此，若我們能夠有一種演算法，可以用以求得感測器的相對位置，如此即可實用於感測器網路中，如此既可避免受到環境的不確定性的干擾，亦可兼顧到實用價值。這也是無已知座標節點感測器存在的定位演算法漸受注目的原因。
相對於一般具有已知座標節點存在的定位演算法，無已知座標節點感測器存在的定位方式可以說是還在起步階段。就我們所知當中，目前所存在的相關演算法皆是以量測距離為主，藉以估測出感測器的相關位置。在本篇論文中，我們進一步試著提出一種結合角度量測以及距離量測的技術，藉由這兩種方式的整合，最後推估出所有感測器存在於區域座標系統當中的座標與方位，以企圖得到更理想的定位結果，我們將詳細敘述於后。

Sensor Network for the past few years has become a very popular research topic due to vigorous development of communication system. Estimation about position of sensors is a fundamental and important link in related researches. Owing to this fact, many different position algorithms were created one after another. However, most algorithm methods need nodes of known coordinates, and most of the information of coordinates needs to be provided by GPS. For this reason, the indoor target environment, interference of environmental materials, and the climate, would all cause possibilities of errors in position. Therefore, the position algorithm without sensors of known nodal coordinates gradually gets researchers’ attention.
In most cases, Sensor Network only needs to know the distance between each other to operate relative applications in network. As a result of this, if we have an algorithm to calculate the relative distance of sensors, we can apply it to Sensor Network in order to avoid interference of uncertainties and achieve practical value. This is also the reason why the position algorithm without sensors of known nodal coordinates gradually wins its own spotlight.
Different from ordinary position algorithm with nodes of known coordinates, the position method without sensors of known nodal coordinates is just in its beginning status. So far as we know, the existed related algorithms all focus on measuring distance to estimate relative distance of sensors. In this thesis, we propose a technique that combines angle and distance measuring. The integration of these two methods is to estimate all coordinates and directions of sensors in local coordinate system. We try to get a more ideal position result. All details are explained on following pages.

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