基於新穎群聚演算法之室內定位系統

Abstract

此研究提出了一種基於新穎群聚演算法的Wi-Fi指紋辨識定位演算法。該技術利用基於支持向量機的新穎群聚方法稱MP-C，我們基於支持向量機邊界的大小進行分類，而不是傳統的方法只參考位置訊號強度重心之間的歐幾里德距離。MP-C創建群聚的定位指紋資料後，我們的定位系統嵌入分類機制來協助定位任務並改善巨大資料庫搜索的問題。此演算法分配測試資料到對應的群集，並用這些對應到的群集資料來估測位置以降低計算複雜度，並過濾掉會影響估測位置的離群資料。我們從實際的無線網絡環境實驗結果證明該方法明顯地提高了定位精度。相對於現有的三個傳統基於群聚的方法，K-均值，親和傳播，與支持向量群聚，分別降低了平均定位誤差達30.34％、30.98％和34.76％。

This study proposes a novel clustering-based Wi-Fi fingerprinting localization algorithm. The proposed algorithm first presents a novel clustering approach based on support vector machine based, namely MP-C, which uses the margin between two canonical hyperplanes for classification rather than the Euclidean distance between two centroids of reference locations’ RSS. After creating the clusters of fingerprints by MP-C, our positioning system embeds the classification mechanism into a positioning task and compensates for the large database searching problem. The proposed algorithm assigns the matched cluster surrounding the test sample and locates the user based on the corresponding cluster’s fingerprints to reduce the computational complexity and remove estimation outliers. Experimental results from realistic Wi-Fi test-beds demonstrated that our approach apparently improves the positioning accuracy. As compared to three existing clustering-based methods, K-means, affinity propagation, and support vector clustering, the proposed algorithm reduces the mean localization errors by 30.34%, 30.98%, and 34.76%, respectively.