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研究生:洪崇嶺
研究生(外文):Chung-Ling Hung
論文名稱:遙測信號之分析、辨識與成像研究
論文名稱(外文):Study on analysis, recognition and rendering of remote signals
指導教授:杜德銘
指導教授(外文):Te-MIng Tu
學位類別:博士
校院名稱:國防大學中正理工學院
系所名稱:國防科學研究所
學門:軍警國防安全學門
學類:軍事學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:128
中文關鍵詞:影像融合強度-色調-飽和度雜訊次空間投影經驗模態分解法六自由度視覺模擬
外文關鍵詞:image fusionintensity-hue-saturation(IHS)noise subspace projection (NSP)empirical mode decomposition (EMD)six degree-of-freedom (6-DOF) visualized simulation
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本研究主要探討高精度衛星影像、水下音響及導彈姿態等三種來源和性質不同的遙測信號,依據信號的特性提出較佳的處理方法。
第一部份說明在影像融合方法中,強度-色調-飽和度融合法(IHS)具有快速處理大量信號資料的能力,應用於IKONOS的影像融合,能得到滿意的空間資訊增強效果,但會產生色彩失真,也稱作頻譜失真。為了解決這個問題,我們提出一個具有頻譜匹配的快速IHS融合法,來改善色彩失真的現象。經實驗結果證明,該方法不僅在處理速度和影像品質上皆優於傳統的IHS融合法。
第二部份,我們針對水下的穩態音頻信號進行特性的理論研究,設計出一個有效可行的分析辨識系統。在系統中,由取出信號頻譜,去除寬頻背景雜訊,再利用峰值分佈法萃取出主要的特徵頻譜作為船艦的聲紋;在比對辨識時,以具有近似最大信號/干擾雜訊比的雜訊次空間投影鑑別法做為分類器進行辨識。經各型船艦資料的實驗證明,本文所提出設計的方法可整合成一個強建性的即時動態船艦辨識系統。
在第三部份,若增加水下音響暫態信號的分析,由於傅立葉轉換和小波轉換,它們的基底函數都是固定的,所以無法滿足水下音響時變信號的分析需求。因此,我們利用經驗模態分解法分析非線性與非穩態的信號,以萃取水下音響信號的特徵,尋求最佳識別的模態分解階數。在實驗中、我們同樣以實際的船艦水下音響信號來驗證,結果證明經驗模態分解法所萃取的水下音響信號特徵,的確能夠提昇後續的判讀作業。
在第四部份,各種導控武器系統在研發的過程中,實體模擬和飛(航)試是很重要的程序。我們利用六自由度視覺模擬,將上述的程序以視覺呈現的方式進行完整的模擬,並作為分析、判斷和驗證導彈性能的輔助工具。六自由度視覺模擬係將實體模擬的六自由度模式以及飛(航)試後的六自由度數值,結合兩種視覺模擬的方式:三維即時成像法與三維回復成像法,將導彈的運動姿態信號,以多媒體視覺模擬的方式來呈現。這兩種方式是互相配合的,在本研究中,以防空及水下導控武器系統作為實驗實例,證明六自由度視覺模擬具有良好的效能。
The adaptive approaches processing distinct remote signals from high resolution space imagery, underwater tones and dynamic motion of guided missile, respectively, are proposed in this dissertation.
Regarding the first part, it is illustrated that Intensity–Hue–Saturation (IHS) technique, among various image fusion methods, represents the capability of quickly merging the massive volumes of data, and can yield satisfactory “spatial” enhancement but may introduce “spectral” distortion in IKONOS imagery. The approach of a fast IHS fusion technique with spectral adjustment is presented to solve this problem, and then demonstrates better performance than the original IHS method by the experimental results, both in processing speed and image quality.
The aim of the second part is at studying underwater stationary acoustic signals and designing a feasible recognition system. In this proposed system, after the signal spectrum extracted, the wideband background noise removed, and the spectrum features detected by a peak selection algorithm ,then a noise subspace projection classifier with approximate maximum signal-to-interference ratio is designed as its recognition. The robustness of the proposed system has been shown in the results of experiments from real ship classes.
With respect to the third part, if it is added the analyzing transient signals in the underwater acoustic signals, Fourier transform and wavelet transform are not satisfied for the requirements of analyzing time-vary underwater acoustic signals, for their basis functions are fixed. Therefore empirical mode decomposition, a useful analysis scheme for non-linear and non-stationary nature signals, is designed to extract the features from underwater acoustic signals for recognition by appropriate decomposed ordes. Through the experiments from diverse ship classes, the results demonstrate the robustness of the proposed method.
In terms of the last part, the processes, hardware-in-the-loop simulation and flight (navigation) test, are essential for varied guided weapon systems in weapon system development. The study of this part adopts six-degree-of-freedom visualized simulation (6DOFVS) which has integrated of 6DOF and two visualized simulation approaches called Real Time Rendering (RTR) and Play Back Rendering (PBR) respectively to display the movement signals of missiles by multimedia, as the visualized display simulating these processes, facilitating analysis, diagnosis and verification of missile functions. Those two approaches supported each other and demonstrate well performance in this study by the instances of anti-air and underwater guided weapon systems.
誌謝 ii
摘要 iii
ABSTRACT v
表目錄 xi
圖目錄 xii
1. 緒論 1
1.1 研究背景 2
1.1.1 高精度衛星影像信號處理 2
1.1.2 水下音響信號的處理 3
1.1.3 導彈運動遙測信號的處理 5
1.2 研究方法 6
1.2.1 快速的IHS融合法 6
1.2.2 水下音響穩態信號的分析 6
1.2.3 水下音響信號(穩態及暫態)的分析 7
1.2.4 導彈運動遙測信號的視覺模擬 8
1.3 論文結構 9
2. IKONOS影像的快速IHS融合技術 10
2.1 RGB-IHS模型轉換 11
2.2 IHS融合法和頻譜失真 15
2.3 IKONOS影像融合法的問題與解決方法 20
2.4實驗結果 23
2.5小結 31
3. 水下音響穩態信號頻譜特徵值之萃取與辨識 32
3.1 音響信號的資料前處理 33
3.2 特徵量可靠性的評估 34
3.2.1 估計值的偏差 34
3.2.2 估計值的變異數(方差) 34
3.2.3 有效估計 35
3.2.4 一致估計 35
3.3穩態信號頻譜的估計 35
3.4寬頻背景雜訊消除 37
3.5特徵萃取 39
3.6比對辨識 41
3.7實驗結果 46
3.8小結 55
4. 利用經驗模態分解法識別水下音響信號 56
4.1 非穩態資料分析方法 57
4.1.1 短時傅立葉轉換(Short-Time Fourier Transform, STFT) 57
4.1.2 小波分析(Wavelet Analysis) 57
4.1.3 Wigner-Ville Distribution(WVD) 58
4.1.4 演化頻譜(Evolutionary Spectrum) 59
4.1.5 經驗正交函數展開(Empirical Orthogonal FunctionExpansion, EOF) 61
4.1.6 其他方法 62
4.1.7經驗模態分解法簡介 63
4.2即時頻率與本質模態函數 65
4.2.1即時頻率 65
4.2.2本質模態函數 69
4.3經驗模態分解法的特徵萃取方式 72
4.4音響信號的資料前處理 73
4.5 篩選過程 75
4.6 解析出IMFs 78
4.7 間歇性準則 80
4.8 模擬實驗 81
4.9 特徵向量 83
4.10 比對辨識 84
4.11 實驗結果 85
4.12小結 93
5. 導彈姿態遙測信號的成像法 94
5.1導彈實體模擬的六自由度模式 94
5.2六自由度模式與視覺模擬整合方式 101
5.3三維即時成像法 101
5.4三維回復成像法 106
5.5實驗結果 110
5.6小結 115
6.結論與未來研究方向 116
6.1結論 116
6.2未來研究方向 117
參考文獻 118
論文發表 126
自傳 128
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