博士 / 國立成功大學 / 系統及船舶機電工程學系碩博士班 / 97 / In this dissertation, pattern recognition techniques are applied to two topics of oceanic technologies, including air radar target recognition and underwater communication localization.
In the first part of this study, pattern recognition techniques are applied to air radar target recognition. We utilize models of ships as targets for identification. The data of radar cross section (RCS) scattered from different types of targets are collected for identification. In general, these RCS data are very complex and are thus difficult to implement recognition. To enhance the recognition ability, we project the RCS data into feature space of PCA (principal components analysis) or LDA (linear discriminant algorithm). The RCS data in this study are obtained through the commercial tool of Ansoft-HFSS software. The original simulated data contain no noise. To make the simulated RCS more consistent with the practical experiment, random noises are added to the simulated data. The existence of random noise will degrade the performance of radar target recognition. In this study, the SVD (singular value decomposition) with Hankel method is applied to separate the noise part from the clean part of RCS data. In addition, the method of CART (classification and regression tree) is combined with the PCA method to give alternative approach of radar target recognition.
In the second part of this study, pattern recognition techniques are applied to underwater communication localization. The communication localization means that the localization is included into the communication system. In other words, the location of the client terminal will be obtained in the communication system by communication signals. Conventional underwater localization systems often utilize approaches based on time of arrival (TOA) or direction of arrival (DOA). Unfortunately, the performances of such approaches are affected by the multi-path reflection. In addition, such approaches require expensive hardware to obtain accurate measurement. To overcome these drawbacks, our localization is based on probabilistic fingerprinting approaches. The approaches are divided into two stages, i.e., the off-line (training) and on-line (predicting) stages. In the off-line stage, signals collected by the sound receiver at different sampling locations are stored to constitute the database (i.e., signal map). In the on-line stage, the real-time signal is measured and the location is thus estimated by comparing the real-time signal with the database. Our localization scheme is based on the probabilistic pattern recognition of acoustic communication signals, but not on ray tracing of signal propagation. Therefore, our underwater localization scheme is not affected by reflected signals. It can tolerate multi-path signals. This will greatly reduce both the hard-ware cost and calibration difficulty in measurement. To enhance the recognition ability of location estimation and reduce the data complexity, all received signals are projected onto the feature space of PCA and LDA. Each projected feature is assumed to have Gaussian probabilistic distributions. Therefore, the location information can be easily obtained by pattern recognition of projected features in PCA and LDA space. In practical measurement, there are severe noisy effects due to the uncertain characteristics in underwater environments. To reduce such noisy effects, the measured signals are processed by the SVD with Hankel technique. Finally, we also utilize the CART technique to obtain efficient pattern matching in the on-line stage. Experiments were successfully conducted in a bounded water pool to verify the benefits of our underwater localization schemes.
This dissertation is divided into five chapters. Chapter 1 gives the introduction of this study. Chapter 2 gives theoretical formulations of pattern recognition utilized in this study. The first part of this study, i.e., the application of pattern recognition to air radar target recognition, is given in Chapter 3. The second part of this study, i.e., the application of pattern recognition to underwater communication recognition, is given in Chapter 4. Finally, the conclusion is given in Chapter 5.
| Identifer | oai:union.ndltd.org:TW/097NCKU5345043 |
| Date | January 2009 |
| Creators | Jhih-sian Ou, 歐致顯 |
| Contributors | Kun-Chou Lee, 李坤洲 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 121 |
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