博碩士論文 104523051 詳細資訊




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姓名 施仲承(Jhong-Cheng Shih)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱 用於量化無線感測網路中直視與非直視訊號模型的EM方法實現目標物定位
(Target Localization with the EM Method for LOS/NLOS Models in Quantized Wireless Sensor Networks)
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摘要(中) 我們研究了無線感測器網路(Wireless Sensor Network, WSN) 在混合直視(Line Of Sight, LOS) 與非直視(Non-Line Of Sight, NLOS) 訊號環境中的目標物定位,由於非直視訊號的影響,會使得原本的定位方法準確度下降,加上考量到感測器資源有限,是以接收訊號強度(Received Signal Strength, RSS) 等數據對其做量化(quantization),融合中心以此量化訊號進行目標物定位。
由於非直視訊號的效應,我們透過建立雙模式高斯混合分佈的測量誤
差,並且假設其混合模型參數是完全未知,因此採用最大期望(Expectation Maximization, EM) 方法來近似目標物位置和混合模型參數的最大似然估計(Maximum Likelihood Estimation, MLE),而我們所提出的方法修正了最小平方法(Least Squares Estimation, LSE) 在測量誤差屬於高斯混合分佈的狀況。
摘要(英) We studied the target localization method for quantized wireless sensor networks(WSN) in the mixed Line Of Sight (LOS) and Non-Line Of Sight (NLOS) signal environments. Owing to the influence of NLOS signals, the accuracy of conventional localization methods are degraded. And, considering limited power resources of sensors,
the Received Signal Strength (RSS) data is usually quantized to several bits. The fusion center can only employ the quantized signals to localize the target position. Due to the effect of non-line of sight signals, we model the measurement noise as a Gaussian mixture distribution and assume that the mixture model parameters are completely unknown. Therefore, the Expectation Maximization (EM) method is used to approximate the Maximum Likelihood Estimation (MLE) target position and the mixed model parameters. The proposed method modifies the Least Squares Estimation (LSE) condition in which the measurement error belongs to a Gaussian mixture distribution.
關鍵字(中) ★ 非直視
★ 高斯混合
★ 量化
★ 最大期望
★ 無線感測器網路
關鍵字(英)
論文目次 目錄
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . ii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . i
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . ii
表目錄 . . . . . . . . . . . . . . . . . . . . . . . . iv
第1 章序論 . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 簡介. . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 章節架構 . . . . . . . . . . . . . . . . . . . . . . 3
第2 章訊號模型和問題描述. . . . . . . . . . . . . . . . . 4
2.1 訊號模型 . . . . . . . . . . . . . . . . . . . . . . 4
2.2 量化訊號 . . . . . . . . . . . . . . . . . . . . . . 7
2.3 問題描述 . . . . . . . . . . . . . . . . . . . . . . 8
第3 章位置估計演算法. . . . . . . . . . . . . . . . . . . 9
3.1 量化訊號的最大似然估計. . . . . . . . . . . . . . . . 9
3.2 引入量化訊號的最小平方估計法. . . . . . . . . . . . . 12
3.3 -law 訊號壓縮法. . . . . . . . . . . . . . . . . . 15
3.4 適用於高斯混合模型的非線性估計. . . . . . . . . . . . 21
3.4.1 高斯混合模型參數的最大似然估計. . . . . . . . . . . 21
3.4.2 最大期望演算法(Expectation Maximization Algorithm, EM) . . . .22
3.4.3 高斯混合模型的位置估計法流程. . . . . . . . . . . . 29
第4 章Cramér–Rao Lower Bound . . . . . . . . . . . . . 31
第5 章高斯-牛頓法與Nelder-Mead 單純型搜索法. . . . . . . .36
5.1 高斯-牛頓法(Gauss-Newton Method) . . . . . . . . . .36
5.2 Nelder-Mead 單純型搜索法(Nelder-Mead Simplex Search Method) . . 40
第6 章系統模擬與結果分析 . . . . . . . . . . . . . . . . 42
6.1 模擬環境說明. . . . . . . . . . . . . . . . . . . . 42
6.2 模擬結果與討論. . . . . . . . . . . . . . . . . . . 46
6.2.1 1bit 量化 . . . . . . . . . . . . . . . . . . . 47
6.2.2 2bits 量化 . . . . . . . . . . . . . . . . . . . 48
6.2.3 3bits 量化 . . . . . . . . . . . . . . . . . . . 51
6.2.4 Floating point 浮點數 . . . . . . . . . . . . . .54
6.3 演算法的收斂曲線 . . . . . . . . . . . . . . . . . . 57
6.3.1 EM 演算法收斂曲線. . . . . . . . . . . . . . . . .57
6.3.2 Nelder-Mead 單純型搜索法、高斯-牛頓法和EM-BFGS 法收斂
曲線和計算時間. . . . . . . . . . . . . . . . . . . . . 57
第7 章結論. . . . . . . . . . . . . . . . . . . . . . . 65
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . 66
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指導教授 張大中(Dah-Chung Chang) 審核日期 2019-8-8
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