本論文以主成份分析、線性鑑別式分析、高斯混合模型及獨立成份分析等多變量統計分析方法應用於雷達目標辨識與水下通訊定位。
本研究的第一部分主要探討多變量統計分析在雷達目標辨識的應用。在雷達目標辨識研究中，我們以船艦為例，利用電磁軟體Ansoft HFSS模擬船艦目標的電磁波雷達散射截面積（Radar Cross Section, RCS），當作辨識依據。基於角度掃描與頻率掃描技術的複雜船艦雷達截面積是一個包含時間、頻率、方位角和極化方向的多變量資料，我們透過主成份分析、線性鑑別分析或獨立成份分析將船艦散射訊號特徵投影展開在彼此低度相關、不相關或獨立的基底空間，以利於辨別出各船艦目標。其次，考慮實際環境雜訊對於本研究的影響，在船艦雷達散射截面積中加入不同訊雜比的雜訊，以更貼近實際量測資料。高度雜訊的存在，不利於船艦雷達目標辨識，因此，我們結合主成份分析、線性鑑別分析以及高斯混合模型應用在基於機率比對的船艦雷達目標辨識。最後，模擬結果顯示本論文所提出的雷達目標辨識能夠達到高辨識率，驗證了本論文將多變量統計方法的結合應用在本研究的可行性。
本研究的第二部分以高斯混合模型應用在基於機率比對的水下通訊定位。在水下通訊定位中，實驗架設採用單一魚探機並利用訊號頻率分量方法，取代實體聲源發射設備。定位流程分成兩個階段，分別為收集訊號以及訓練並建立資料模型的離線階段和實際測試並比對訊號相似度的線上階段。在離線階段中，預先在不同取樣位置接收水下通訊訊號。應用高斯混合模型將各組資料建立機率模型並儲存至資料庫；在線上階段中，我們利用最大似然法將即時接收到的訊號與資料庫中的訊號做比對，來估算出目前接收訊號者的位置。最後，模擬結果顯示本論文所提出的水下定位機制能夠達到高定位準確率，驗證了本論文將多變量統計方法的結合應用在本研究的可行性。
本論文共分為八章。第一章為緒論，介紹文獻回顧、研究動機、研究貢獻及論文架構。第二章介紹雷達掃描技術及多變量統計分析方法，其中，雷達掃描技術包括角度掃描技術及頻率掃描技術；多變量統計分析方法包括主成份分析、線性鑑別式分析、高斯混合模型及獨立成份分析。第三章實現基於投影頻率掃描雷達截面積特徵之船艦雷達目標辨識。第四章應用獨立成份分析在基於主成份分析之雷達目標辨識。第五章應用獨立成份分析於頻率掃描雷達截面積之實現。第六章結合主成份分析、線性鑑別分析以及高斯混合模型應用在基於機率比對的船艦雷達目標辨識。第七章以高斯混合模型應用在基於機率比對的水下通訊定位。第八章為本研究的結論以及未來研究建議的方向。

In this dissertation, multivariate statistics analysis including PCA (principal components analysis), LDA (Linear Discriminant Analysis), GMM (Gaussian Mixture Model) and ICA (Independent Component Analysis) are applied to RCS (radar cross section) based radar target recognition of ships and underwater localization.
In the first part of this study, the radar target recognition of ships is given based on projected features of angular-diversity or frequency-diversity RCS. The simple models of ships are considered as the targets for recognition. The data of RCS scattered from different types of targets are simulated by the commercial tool of Ansoft-HFSS software. In general, the RCS data that depends on time, frequency, the elevation angle and the azimuth angle are complex and thus are difficult to implement recognition. To extract much of the important information contained in the second order or high order dependencies, multivariate statistics analysis including PCA, LDA, GMM and ICA are applied to RCS based radar target recognition of ships in this study. The RCS data are projected into features space of PCA, LDA or ICA. To make the simulation more consistent with the practical experiment, the noise effects are also taken into consideration. Therefore, the RCS data including random noise are nonstationary and will degrade the performance of recognition. The prediction rule based on a distance function of the features with respect to feature center of a class in the recognition of radar targets under noisy environments becomes unreliable. In this study, the method of GMM is combined with the PCA method and LDA method to achieve reliable radar target recognition of ships.
In the second part of this study, underwater localization is given by Gaussian mixture model based probabilistic approach. We utilize frequency component-based approach based on the pattern recognition of maximum likelihood with Gaussian mixture model to treat the underwater localization. We consider that the underwater localization scheme is the problem of probabilistic pattern matching. Therefore, it is not affected by reflection cased by multi-path signals. The whole process is divided into two parts including offline phase (i.e., offline training stage) and online phase (i.e., online matching stage). The aim of probabilistic pattern matching is to predict the accurate position of the unknown location by comparing the signal vector captured during the online phase with the radio map database built during the offline phase. Gaussian mixture model is successfully utilized to treat the measured underwater signals and obtained accurate location information. In addition, we can save a lot of time to gather the underwater acoustic signals in a bounded water pool and reduce the computation complexity.
This dissertation is divided into eight chapters. Chapter 1 presents an introduction of this study. In Chapter 2, the basic theory of radar cross section of ships and multivariate statistics analysis which consists of PCA, LDA, GMM and ICA are introduced. In Chapter 3, the radar recognition by projected features of frequency diversity RCS is presented. An application of ICA technique to PCA based radar recognition is given in Chapter 4. In Chapter 5, the frequency-diversity RCS based target recognition with ICA projection is presented. Chapter 6 shows maximum likelihood with applications to RCS transform based radar target recognition. In Chapter 7, an underwater localization by Gaussian mixture model based probabilistic approach is presented. Finally, a conclusion to the dissertation is given in Chapter 8. Also, further improvements and future works are described.

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