本論文的研究重點為兩大部分分別為水上雷達目標辨識與水下通訊定位，利用核方法(即核主成分分析 kernel principal component analysis, KPCA 與核散佈差鑑別分析 kernel scatter-difference-based discriminant analysis, KSDA) 及最大散佈差 (maximum scatter difference, MSD) 作為成功準確率的依據。
在水上雷達目標辨識研究中，目標選定為簡易的船艦模型 (即漁船、軍艦及貨櫃輪) 於高低起伏的海平面上進行模擬，採用商業電磁軟體Ansys HFSS計算海平面上船艦的角度掃描及頻率掃描的雷達散射截面積 (radar cross section, RCS)，當作辨識的依據。電磁波RCS本身與目標的形狀、尺寸、結構及材料有關，也與入射電磁波的頻率、入射角、極化方式等有關，這些資料通常過於龐大且無規則性，不易於儲存分析辨識。因此，為了簡化RCS散射資料以利於雷達目標辨識，本研究透過KPCA、KSDA及MSD作為水上雷達目標辨識。KPCA與KSDA的原理為將原始資料投影至高維度空間中做線性演算法(即主成分分析 principal component analysis, PCA與MSD)，而MSD為費雪線性鑑別分析 (Fisher linear discriminant analysis, FLDA)的改良該算法複雜度降低，加速了求解的運算過程。最後，利用歐氏距離 (Euclidean distance) 計算出船艦之類型，由數值模擬結果可知應用此三種演算法能夠達到高辨識率於水上雷達目標辨識。為了更貼近實際量測的結果，我們主動加入了高斯分佈的隨機變量作為雜訊，由數值模擬結果可知能夠有效降低雜訊的干擾，驗證了本論文將KPCA、KSDA及MSD方法應用在此研究主題的可行性。
在水下通訊定位研究中，為了避免反射訊號對量測結果的影響，引用指紋特徵比對法 (location fingerprinting) 的概念。為了證明此想法能夠不受反射訊號或多重路徑傳播訊號所帶來的影響，實驗設置在有邊界的拖曳水槽 (towing tank)。我們提出了頻率分量來模擬水下通訊訊號發射器 (sound projector) 的方法，藉此降低硬體成本問題於水下環境中。實驗主要分為訓練與測試狀態，將收集到的水下通訊訊號應用KPCA的方法來做參數訓練。在測試狀態，用最大似然 (maximum likelihood, ML) 來估算出水下通訊訊號的接收位置座標。最後，利用歐氏距離計算出實際位置和估算位置之間的距離來當作定位誤差於水下環境。實驗結果顯示，利用概率模式識別於KPCA的特徵空間，來實現無線水下通訊定位是可行的。
本論文共分為九章。第一章為緒論，介紹研究背景、動機、論文架構及研究貢獻。第二章為演算法理論介紹，包括核主成份分析、核散佈鑑別分析及最大散度差。第三章、第四章及第五章為應用核主成分分析、核散佈鑑別分析及最大散度差於角度掃描雷達散射截面積。第六章及第七章為應用核主成分分析及核散佈鑑別分析於頻率掃描雷達截面積。第八章為結合核主成份分析及最大似然法應用在基於機率比對的水下通訊定位。第九章為本研究的結論以及未來研究建議的方向。

This dissertation focuses on two points: air radar target identification and underwater communication positioning. Kernel method (i.e. kernel principal component analysis, KPCA and kernel scatter-difference-based discriminant analysis, KSDA) and maximum scatter difference (MSD) are used as the basis for determining success and accuracy rates.
In the research on air radar target identification, simple ship models (fishing boat, naval ship and container vessel) are chosen as the targets, simulations were conducted on undulating sea level, and commercial electromagnetic software Ansys HFSS is utilized to calculate angular-diversity and frequency-diversity radar cross section (RCS) as basis of identification. RCS of electromagnetic wave is related to not only shape, dimension, structure and material of target but also frequency, incidence angle and polarization mode of incident electromagnetic wave. These data are usually enormous, irregular and difficult to store, analyze and recognize. Therefore, for the sake of simplifying RCS scattering data and thus benefiting radar target identification, this dissertation applies KPCA, KSDA and MSD to air radar target identification. Principle of KPCA and KSDA is to project original data into high-dimensional space and then perform linear calculation (i.e. principal component analysis, PCA and MSD). MSD, an improved version of Fisher linear discriminant analysis (FLDA), reduces complexity of calculation and accelerates computing process. Finally, Euclidean distance is used to calculate type of the ship. From numerical simulation results we know these three kinds of calculations can achieve a high air radar target identification rate. To be closer to actual measurements, we actively add random variables of Gaussian distribution as noises. Results of numerical simulation show noise-caused disturbances are effectively decreased and verify feasibility of applying KPCA, KSDA and MSD to theme of this study.
In the research on underwater communication positioning, the concept of location fingerprinting is introduced so as to avoid influences of reflected signals on measuring results. To demonstrate such idea is not affected by reflected signals or multipath communication signals, experiments are done in bordered towing tank. We propose a method of using frequency component to simulate sound projector of underwater communication to lower hardware cost in underwater environment. The experiment consists of training and test state. The collected underwater communication signals are subject to parametric training through KPCA. Under test state, maximum likelihood (ML) is used to estimate coordinate of reception location of underwater communication signal. Finally, Euclidean distance is used to calculate distance between actual location and estimated location as positioning error in underwater environment. Experimental results show it’s possible to realize wireless underwater communication positioning by an application of probability model identification into feature space of KPCA.
This dissertation is divided into nine chapters. The first chapter is Introduction. The second chapter is an explanation of algorithmic theories including KPCA, KSDA and MSD. The third, fourth and fifth chapters apply KPCA, KSDA and MSD to angular-diversity RCS. The sixth and seventh chapters apply KPCA and KSDA to frequency-diversity RCS. The eighth chapter combines KPCA and ML and apply them to underwater communication positioning based on probability comparison. The ninth chapter is conclusion of this dissertation and recommended direction of future research.

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