臺灣博碩士論文加值系統

近年來關於室內環境的定位服務需求提高，因此室內定位技術逐漸受到關注，在眾多定位技術中，以Wi-Fi為訊號源的特徵指紋演算法最為廣泛採用，因為其容易實踐及同時能夠提供網路服務的特點，然而人體的阻擋及訊號源不穩定的訊號強度等時變因素限制了室內定位服務的性能，除此之外，特徵指紋演算法須事先收集一定數量的參考點位置及訊號強度作為資料庫，此過程相當費時，為了解決這些問題，我們提出了以訊號強度為主地圖為輔之參考點分群演算法將參考點分類並提供理想的監控點數量及位置，其功能為長時間蒐集訊號強度資訊，不同於大部分的分群方法，以訊號強度為主地圖為輔之參考點分群演算法是根據參考點的變化趨勢來分群，接著利用基於分群之線上資料庫建立演算法建立一個即時的特徵指紋資料庫，而重建後的資料庫在時變的環境下依舊能夠達到所需的定位精準度，此外，本研究提出了基於分群之特徵縮放加權最近鄰居法作為定位的方法，性能評估顯示在時變的環境中，結合基於分群之線上資料庫建立演算法及基於分群之特徵縮放加權最近鄰居法能夠比過期的資料庫提供更精準的位置估計。

Location estimation has received extensive attention in the view of the
emerging demand for Location-Based Service (LBS) in indoor environments.
WiFi fingerprinting is the most commonly used technique for its simple implementation and its ability to provide networking service. However, timevarying factors such as human effect and unstable received signal strength
(RSS) from Access Points (APs) limit the performance of indoor LBS. Besides, to collect a suitable number and physical locations of Reference Points
(RPs) is time-consuming for the fingerprinting system. To address the problem, we propose the RSS-Oriented Map-Assisted RPs Clustering (ROMARC)
algorithm to cluster RPs and provide appropriate numbers and locations of
monitor points (MPs) where receive RSS all the time. Different from most
of the clustering schemes for RSS fingerprinting system, the ROMARC algorithm is designed to find RPs which have a similar variation of RSS value.
Then, cluster-based online database establishment (CODE) algorithm adopt
learning-based regression algorithm to construct a real-time database based
on results of ROMARC algorithm. The result obtained by CODE algorithm
can achieve the required positioning accuracy. Furthermore, we propose the
cluster-based feature scaling weighted KNN (CFS-WkNN) algorithm to estimate target’s location. For performance evaluation, simulation and implementation results shows that our proposed system can provide better location
estimation than the expired database in the time-variant environment.

Chinese Abstract i
English Abstract ii
Acknowledgement iii
Contents iii
List of Figures v
List of Tables vii
1 Introduction 1
2 System Architecture and System Model 5
2.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Proposed RSSI-based Indoor Localization Algorithms 10
3.1 Proposed RSS-Oriented Map-Assisted RPs Clustering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Proposed Cluster-based Online Database Establishment Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Proposed Cluster-based Feature Scaling Weighted KNN Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Simulation Results 18
5 Experimental Results 25
6 Conclusion 32
Bibliography 33

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