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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/360

    Title: 應用於室內定位之非監督式無線訊號指紋圖資建立方法;Unsupervised radio map learning for indoor localization
    Authors: 張瑋池;CHANG, WEI-CHI
    Contributors: 電機工程研究所
    Date: 2017
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 過去幾十年大多數的室內定位方法多以基於Wi-Fi無線訊號指紋(Fingerprint)的定位技術為主,因為這項技術擁有低設備成本以及有效處理遮蔽問題的能力,基於無線訊號指紋的定位方法主要分成兩個階段,訓練階段與定位階段。訓練階段的目的為建立無線訊號指紋圖資(Radio Map),建立的方法是在已知的參考位置上收集 Wi-Fi 訊號強度;定位階段則是利用當下裝置接收到的Wi-Fi強度與訓練階段的訊號指紋圖資做相似性量測得到定位結果。但若要在大範圍的室內環境提供定位服務,將需要耗費相當多的時間與人力去收集訊號強度建立訊號指紋資料庫,為了克服此問題,本論文提出一個應用於室內定位之非監督式的訊號指紋資料庫建立方法,有別於其他方法利用模擬的指紋圖資或事先取得訊號傳播模型的方式來減少花費過多人力與時間的問題,我們所提出的方法利用Wi-Fi訊號以及慣性元件訊號來幫助自動建立無線訊號指紋圖資,將慣性元件訊號處理後得到多名使用者的行走軌跡資訊與當下收集的無線訊號強度,針對不同使用者提供的資訊,利用無線訊號標記初步串接所有軌跡,並且結合四項限制來建立最佳無線訊號指紋圖資,包含軌跡無線訊號強度岐管對齊限制(manifold-based smooth)、無線訊號標記對齊限制(landmark alignment)、軌跡間相互定位限制(inter-trajectory)與位移限制(displacement),由四項限制設計一個建立無線訊號指紋圖資的迭代優化程序,藉此程序建立完整的無線訊號指紋圖資。
    For radio-based indoor localization, the approaches founded on the radio fingerprint concept are efficient duo to low cost and the ability to handle occlusion effects. However, the approaches require a lot of human labor to label training data for radio map (fingerprint) construction. To address this issue, in this paper, we proposed an unsupervised framework to learn a Wi-Fi radio map in an indoor environment. Unlike conventional approaches that depend on a simulated radio map or a prior radio propagation model to reduce human efforts, our method uses Wi-Fi and IMU signals collecting by crowdsourcing to build a robust radio map automatically. More concretely, four types of constraints are fused by the proposed radio map optimization procedure. They include the alignment of Wi-Fi landmarks, the displacement constraint, the manifold-based smooth constraint, and the inter-trajectory constraints. Our experiment results also show the effectiveness of the unsupervised radio map.
    Appears in Collections:[電機工程研究所] 學位論文

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