臺灣博碩士論文加值系統

無線網路技術已經不止於二維平面(Two-Dimensional)的運用；其中，三維空間的定位(Three-Dimensional localization)尤其備受矚目。基於對低頻寬、高準確率及高同步誤差容錯力(high tolerance towards synchronization errors)的需求，我們提出一個嶄新的感測器部屬模式。運用分散式的到達時間差感測器陣列(distributed time difference of arrival-based sensor array)，我們創造一個3D虛擬網格定位系統來應對在立體空間中定位多個目標物的難題。我們接著將評估位置的問題轉換成解碼的問題，並利用基於機器學習的渦輪解碼來取得與目標物最相
關的立方格。我們的貢獻主要有二:
一) 我們提出基於渦輪期望值傳播的解碼演算法(turbo expectation propagation-based decoding algorithm, TED)。期望值傳播演算法是貝式機器學習(Bayesian machine learning)裡高效的演算法之一。
二)我們提出一個改良版的TED演算法。該演算法運用了迭代感測器可信度校正(iterative sensor reliability correction,ISRC)，因此稱之為ISRC-TED。確切來說，ISRC-TED透過在每次迭代感測器的決策來增加解碼的準確性，因此表現優於TED。
此外，TED及ISRC-TED的基於樹狀搜索的低複雜度解碼演算法也被提出。在我們模擬的結果中，我們所提出的TED及ISRC-TED演算法十分出色，尤其是在感知網路處於省電模式的時候。

Wireless techniques have progressed beyond two-dimensional (2D) applications; in particular, three- dimensional (3D) localization in wireless sensor networks has been attracting increasing attention. Motivated by the need for low bandwidth, high accuracy, and high tolerance toward synchronization errors, we propose an innovative sensor deployment scheme that uses distributed time difference of arrival–based sensor arrays to create a virtual cube-based location system in 3D space to address the multitarget 3D localization problem. After transforming the location estimation problem into a decoding problem, we utilize machine learning– based turbo decoding to acquire the cubes that are most likely to be associated with the targets’ locations. Our contributions are twofold. First, we propose a turbo expectation propagation (EP)-based decoding algorithm (TED). EP computation is an efficient tool in Bayesian machine learning. Second, we propose an improvement to the TED algorithm, in which iterative sensor reliability correction (ISRC) is used; this improved algorithm is referred to as ISRC- TED. Specifically, the ISRC-TED algorithm outperforms the TED algorithm by utilizing the decisions of the sensor array at every iteration to further improve the decoding accuracy. A low-complexity tree search–based decoding strategy for TED and ISRC-TED is also proposed. In simulations, the proposed TED and ISRC-TED algorithms were highly effective, especially when the partial sensor network was in power-saving mode.

論文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . III
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VI
表目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IX
1 緒論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 系統模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 基於渦輪期望值傳播解碼演算法 (Turbo EP­Based Decoding Algorithm) . . . . 11
4 模擬結果 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5 結論 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
授權書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

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