在大型網路下以群簇法為基礎的樣本比對定位法之研究

Abstract

在定位服務裡，系統的反應時間是一個關鍵點，對於即時性的應用來說，更是如此。在大型網路下（如無線城市），以樣本比對法為基礎的定位系統，如此的需求更為明顯。此類定位法的運作是仰賴目前物體收集到的訊號強度特徵與事先在訓練階段建立的以訊號強度為樣本的資料庫做比對來達到定位的目的。在這篇論文中，我們提出一個以群簇法為基礎的樣本比對定位架構來加快定位的程序。藉著將擁有類似的訊號特徵樣本的訓練點群聚在一起，我們會展示如何降低定位所需的比較複雜度來加速整個定位的流程。為了解決訊號飄移的問題，我們更提出了幾個有效的分群法。在許多廣泛的模擬的結果下，我們可以發現：平均來說，在不影響定位準確度的情況下，我們提出的系統相較於原來的樣本比對法的比較複雜度上可減少至少90%。

In location-based services, the response time of location determination is critical, especially in real-time applications. This is especially true for pattern-matching localization methods, which rely on comparing an object's current signal strength pattern against a pre-established location database of signal strength patterns collected at the training phase, when the sensing field is large (such as a wireless city). In this work, we propose a cluster-based localization framework to speed up the positioning process for pattern-matching localization schemes. Through grouping training locations with similar signal strength patterns, we show how to reduce the associated comparison cost so as to accelerate the pattern-matching process. To deal with signal fluctuations, several clustering strategies are proposed. Extensive simulation studies are conducted. Experimental results show that more than 90% computation cost can be reduced in average without degrading the positioning accuracy.