| 研究生: |
邱文勝 Chiu, Wen-Sheng |
|---|---|
| 論文名稱: |
到達時間與到達角度之量測及聲源反算之研究 Research on measurement of arrival time and arrival angle and source inverse calculation |
| 指導教授: |
涂季平
Too, Gee-Pinn |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 系統及船舶機電工程學系 Department of Systems and Naval Mechatronic Engineering |
| 論文出版年: | 2007 |
| 畢業學年度: | 95 |
| 語文別: | 中文 |
| 論文頁數: | 119 |
| 中文關鍵詞: | 聲源定位 、到達角度 、類神經網路 |
| 外文關鍵詞: | source localization, Artificial Neural Network, Arrival angle |
| 相關次數: | 點閱:100 下載:4 |
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本文分為三個小主題,第一,利用本系現有的水中聲學設備並配合National Instruments(美商國家儀器公司)生產之資料擷取卡等設備,實際建立一套M序列聲源與設計聲源訊號可發、麥克風可收之系統架構。並將此架構實現於拖航水槽中,將所設計的M序列聲源與設計聲源訊號在距離100公尺由水下麥克風或J9聲源發射器發射,經由水下麥克風接收,將所接收到之訊號做訊號處理以及交叉相關函數的計算,進而算出聲源訊號之到達時間。第二,利用國科會海洋聲學整合型計畫出海工作佈放垂直線陣列(Vertical Line Array, VLA)所收錄到的船舶噪音做為聲源並透過波束形成(Beamforming)計算出海中實驗之延遲時間與到達角度,並且以不同的訊號處理方式以及交叉相關函數的計算,得到不同的延遲時間與到達角度,並且做比較。第三,探討以聲線理論 ( Ray Theory ) 為基礎的淺海水聲被動定位法。運用聲線理論建立水下聲通道 ( channel ) 數學模型,分析接收器所接收到的物理量(到達角度),利用類神經網路解析連結聲源與接收器之間聲線物理量之相互關係,以達到求解未知聲源位置的目的。此主題探討聲線理論應用於淺海外,並應用類神經方法達到聲源模擬與定位的架構,配合非訓練數據集的新聲源點進行模擬,以驗證本論文定位方法與流程之正確性與可靠性。
The paper contains three parts, First, by using underwater acoustic equipment with Data Acquisition System (made by National Instruments), an M-sequence source and a hydrophone receiving system is established and tested in the towing tank to measure arrival time from source to receiver. The system is tested by use of hydrophone and J9 sound source in the water tank with one hundred in distance. Cross-correlation functions between source signals and received signals are evaluated to obtain the arrival time of acoustic signal.
Second, ship noise was collected by the vertical line array (VLA) in the sea test of acoustics experiment in 2006, Beamforming theory is used to evaluate delay time and arrival angle.
Third, a passive localization for shallow water acoustic sources is developed in this thesis. Ray acoustic theory is used to establish underwater acoustic channel and to analyze physical quantities (arrival angle) received by sensors. Artificial neural network (ANN) is developed based on the input data (the arrival angle) and output data (the source location), The result of this network system is verified by using a non-trained source to prove its accuracy and reliability.
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