GPS (Global Position System) 是目前非常普遍的一種定位技術，其相關的應用也不斷的在增加。可惜的是由於衛星訊號容易受到建築物的遮蔽而失去訊號，所以GPS比較適合使用於室外，在室內的定位上比較不合適。因此近幾年發展出了不少的室內定位技術,其中又以LANDMARC這個系統最為受到注意，也是本論文研究的重點。
LANDMARC是一套以無線射頻辨識 (RFID，Radio Frequency IDentification)為基礎的定位系統。在我們實作LANDMARC的過程中，有發現一些不尋常的大誤差出現在傳統的LANDMARC上。因此在本文中我們提出了兩個主要的處理方法。第一個方法是捨近求遠法後加權調整，以減少因為距離和接收訊號強度之間的非線性關係，所產生的一些影響。第二種方法是一種有別以往的處理方式，在此我們稱之為距離定位型LANDMARC，而傳統的LANDMARC稱之為強度定位型LANDMARC。這兩種方法均能夠明顯的提升定位的精準度，其中距離定位型LANDMARC的效能又比捨近求遠法後加權調整的方法更加卓越。
在論文最後我們用密度的觀念模擬比較各種拓撲的形式和不同的k之間的關係;試著找出最好的效能組合。從我們的各種模擬中,所得到的結論是方形的拓樸和k=4可以達到最好的整體效能。

GPS (Global Position System) is a very popular location technology, but because the satellite signals may easily be obscured by buildings, GPS is not suitable for use in indoor location. The LANDMARC system is radio frequency identification (RFID) based location system and has attracted great attention recently. In our implementation of the LANDMARC system, we have observed that some unusual large errors occur in the traditional LANDMARC. Thus, in this study, we proposed ways of resolving these problems. There are two major ideas proposed in this study. The first one is to forsake the nearest reader with normalized weights to reduce the effects of nonlinear relationship between the tag distance and the received signal strength difference. The second one is to use another completely different calculation algorithm. This new approach is based on the distance where the traditional one is based on power. These two new methods can indeed improve accuracy apparently. It can be found that the distance based approach can have better performance than that of using forsaking the nearest reader with normalized weights. Finally, with dense points considered in the simulation, different topologies and various k values are tested to see which topology and which k value can have the best performance. From our simulation, it is concluded that the square topology and k=4 can achieve the best performance overall.

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