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資訊電子學刊

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篇名 以RSSI預測模型為基礎之室內LBS 技術的研究
卷期 1:1
並列篇名 A Research for Indoor LBS Technology Based on RSSI Prediction Model
作者 葉生正邱奕世彭詠靖
頁次 001-008
關鍵字 接收訊號強度值WiFiLBS訊號紋樣式比對預測模型RSSIFingerprintingPattern matchingPrediction model
出刊日期 200603

中文摘要

WiFi 的發展與應用在近年來相當受到青睞,其優勢已經不僅是在無線存取網路上的方便性與彈性,於LBS(Location-Based Service) 定位服務方面的應用與技術上也日趨成熟。特別是在2000 年IEEE INFOCOM 由P. Bahl 和V. N. Padmanabhan 提出名為RADAR的系統,就是應用WiFi 環境的無線電波訊號特徵,進行行動端之定位與追蹤運算,以達到即時位置感知 (Location-awareness) 的目的。本論文主要是在無線區域網路中,分析無線電波訊號強度在室內空間的分佈趨勢,再利用Matllab 之Polyfit 函數建立訊號傳遞之預測模型,且於連線階段將即時收到的無線電波訊號強度值(RSSI)代入此模型後,經由選根等機制來進行定位演算與精確地位置判斷。目前實驗結果顯示,在定位誤誤距離小於5 公尺範圍內之機率約可達88%,並能有效降低一般位置感知系統於離線階段建立訊號紋(Fingerprinting) 的成本,以及行動端裝置之運算負擔。

英文摘要

The proliferation of WiFi applications has forested a growing interesting in recent years. Not only the convenient for wireless accessing internet but also the Location-Based Services (LBS) have been developed well. P. Bahl and V. N. Padmanabhan proposed a paper named RADAR in 2000 IEEE INFCOM. RADAR, a radio-frequency (RF) based system for locating and tracking users inside building. My research was based on Wireless LANs of an indoor environment. I observed the RF propagation model to find a second order polynomial which is adopted to generate a prediction curve. During the on-line phase we gather received signal strength indicator (RSSI) as the substitution from multiple base stations with Matlab Polyfit function to calculate location estimation. As the experimental results of my research which indicated that the 88 percentile of the error distance within 5 meters. According to the proposed radio prediction model, we can
reduce the number of training points during the off-line phase, and improve the computing load of mobile devices.

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