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研究生: 陳秉聖
Chen, Bing-Sheng
論文名稱: 小波及經驗模態分解去雜訊之模擬及其於船舶目標辨識之應用
Noise Reduction Simulation Based on Wavelet and Empirical Mode Decomposition and Its Application to Recognition of Ship Target
指導教授: 李坤洲
Lee, Kun-Chou
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: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 72
中文關鍵詞: 雷達目標辨識雷達散射截面積卷積神經網路小波閾值經驗模態分解S-G 濾波器
外文關鍵詞: Radar Cross Section, Convolutional Neural Network, Wavelet Threshold, Empirical Mode Decomposition, S-G Filter
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  • 摘要 I 英文延伸摘要 II 誌謝VI 目錄VII 表目錄 IX 圖目錄 X 第一章 緒論 1 § 1.1 研究動機與目的 1 § 1.2 文獻回顧 2 § 1.3 論文架構 3 第二章 相關理論 6 § 2.1 電磁散射理論 6 § 2.1.1 散射截面積 6 § 2.1.2 雷達散射截面積 7 § 2.2 機率理論 8 § 2.2.1 常態分佈 8 § 2.3 類神經網路 8 § 2.3.1 基本架構與概念 8 § 2.3.2 前向傳播 9 § 2.3.3 反向傳播 9 § 2.4 卷積神經網路 10 § 2.4.1 卷積神經網路架構與概念 10 § 2.4.2 卷積層 11 § 2.4.3 池化層 11 § 2.4.4 轉置卷積網路 11 § 2.5 雜訊消除法 --- 小波閾值降噪 12 § 2.5.1 小波閾值降噪概念 12 § 2.5.2 小波轉換 12 § 2.5.3 閾值降噪 13 § 2.5.4 模擬結果 14 § 2.6 雜訊消除法 --- 經驗模態分解結合S-G濾波器降噪 14 § 2.6.1 經驗模態分解結合S-G濾波器降噪概念 14 § 2.6.2 經驗模態分解 15 § 2.6.3 S-G濾波器 16 § 2.6.4 模擬結果 17 第三章 應用降噪技術於船舶目標辨識 32 § 3.1 雷達散射截面積數據處理流程 32 § 3.2 卷積神經網路架構 33 § 3.3 無雜訊數據分析與討論 34 § 3.3.1 角度採集法數據訓練雷達目標辨識模型 34 § 3.3.2 頻率採集法數據訓練雷達目標辨識模型 34 § 3.4 雜訊設置與產生 35 § 3.5 應用小波閾值降噪技術 35 § 3.5.1 應用小波閾值降噪於含雜訊數據與訓練 35 § 3.5.2 用角度採集法數據訓練之雷達目標辨識模型 36 § 3.5.3 用頻率採集法數據訓練之雷達目標辨識模型 36 § 3.6 應用經驗模態分解結合S-G濾波器降噪技術 37 § 3.6.1 應用驗模態分解結合S-G濾波器於含雜訊數據與訓練 37 § 3.6.2 用角度採集法數據訓練之雷達目標辨識模型 37 § 3.6.3用頻率採集法數據訓練之雷達目標辨識模型 38 第四章 結論與未來展望 66 § 4.1 結論 66 § 4.2 未來展望 67 參考文獻 71

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