臺灣博碩士論文加值系統

在無線感測網路中，我們套用許多定位演算法在室內定位系統上。然而發現許多演算法的計算複雜度太高，不適合應用在感測器。舉例而言，粒子濾波演算法的先天的限制，造就粒子濾波器只能以少量的取樣點進行定位。伴隨而來的結果則是高定位誤差。因此，需擬定新定位計劃以提高定位準確度。本論文利用支持向量迴歸的觀念抑制估測錯誤，同時增強定位系統的可靠性，而創出內核粒子濾波演算法，此演算法主要遵循三個步驟:(1)初始支持向量迴歸估測、(2)內核重新加權，和(3)估測精緻化。實驗結果顯示此論文主要可利用少量取樣點即可達到優良的室內定位準確度，以及三顆參考點的KBPF演算之效能可匹配四顆參考點的KLF演算法定位效益。

In the wireless sensor network, several localization algorithms have been proposed for indoor positioning systems. However, the computational complexity of these schemes is high, which may not be suitable to be implemented in sensor nodes. For example, the limited sensor capabilities lead to performing the particle filtering with a very small set of samples, which results in high positioning errors. Hence, a novel sampling scheme may be required to improve estimation accuracy for the particle filter method. In this thesis, the concept of support vector regression (SVR) is conducted to suppress the estimation error, which enhances the reliability of the positioning system. Accordingly, we propose a Kernel-Based Particle Filtering (KBPF) algorithm, which consists of the following three steps: (1) Initial SVR Estimation; (2) Kernel-based Re-weighting; and (3) Estimation Refinement. The experimental results show that the proposed scheme can achieve good indoor positioning accuracy with a small number of samples and the performance of the proposed KBPF system using three beacons is comparable with that of the KLF system using four beacons.

致謝 ii
中文摘要 iii
Abstract iv
List of Tables v
List of Figures vi
1. INTRODUCTION 1
2. THE INTEGRATED LOCALIZATION SYSTEM 3
2.1 System Model 4
2.1.1 Wireless Sensor Network 5
2.1.2 Gateway 9
2.1.3 Server 11
2.2 Algorithms 13
2.2.1 Kite Location Fix(KLF) 13
2.2.2 Particle Filter 16
2.2.3 Kernel-Based Particle Filter 19
3. Kernel Functions 23
3.1 Selection of Kernel Functions 24
3.2 Gaussian Kernel-Based Particle Filter 28
3.3 Quadratic Kernel-Based Particle Filter 29
3.4 Norm Kernel-Based Particle Filter 31
4. Experimental Results 33
4.1 Hardware Specifications 33
4.2 Environmental Settings 36
4.3 Results 40
5. CONCLUSION 45
REFERENCES 46

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