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題 名 | Time Delay Estimation Using Time Domain LMS Filtering Algorithm=時間領域最小均方值演算法 |
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作 者 | 黃啟輝; 李泰興; 楊先仁; 林鑫龍; | 書刊名 | 遠東學報 |
卷 期 | 26:3 2009.09[民98.09] |
頁 次 | 頁479-484 |
分類號 | 448.5 |
關鍵詞 | 最小均方值; 適應性濾波器; 時間延遲估測; Least mean square; Adaptive filter; Time delay estimation; |
語 文 | 英文(English) |
中文摘要 | 本研究以最小均方值演算法(Least Mean Square, LMS)為基礎,配合時間領域適應性濾波器來實做時間延遲估測。估測兩個空間性分離感測器接受到信號之時間差可應用在許多應用上。此一時間差可以用有限脈衝響應(Finite Impulse Response ,FIR)濾波器為模型,其加權係數為sinc 函數之取樣。以適應性演算法估測出最大之加權係數值為整數之時間延遲估測,再應用直接延遲估測(Direct Delay Estimation, DDE)公式,計算出非整數之時間延遲估測。以延遲時間D =1.2 sec 為例,計算出真實值sinc (1-1.2) =0.93548. 比較寬頻信號源(Broad Band Source)與窄頻信號源(Narrow Band Source)環境之時間延遲估測,發現寬頻信號源環境之最大加權係數估測值會快速收斂且接近真實值,時間延遲估測值為D=1.24 sec; 窄頻信號環境時間延遲估測值為D=1.44 sec。寬頻信號源會受到背景雜訊(backgroundnoise)之影響,但是窄頻信號源除了被背景雜訊之外,還有信號之相關性(signal correlation)之影響。 |
英文摘要 | The research was based on Least Mean Square (LMS) and time domain adaptive filter to implement the time delay estimation. The need to estimate time delay (or difference) between signals received by two spatial separated sensors arises in many applications. The time difference between two sensors can be modeled as a finite impulse response (FIR) filter whose weight coefficients are samples of a sinc function. When the weight coefficients are estimated, the having the largest amplitude. We can use the so-called direct delay estimation (DDE) formula or the interpolation formula for extracting the non-integer time delay. For the example the time delay D=1.2 sec, calculated the value of tap weight sinc (1-1.2) = 0.935489. To verify and compared the adaptive LMS filtering algorithm for TDE under different environments (broad band and narrow band signal). For the broad band source signal case, the maximum tap weight could close to true weight and the time delay closed to D=1.24 sec, for the narrow band source signal case the time delay closed to D=1.44 sec. The causes of the results were because the former was affected by the effect of background noise while the later was by both the effect of background noise and relative large eigenvalue spread. |
本系統中英文摘要資訊取自各篇刊載內容。