基于数据挖掘技术的目标识别分类研究
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摘要
数据挖掘是知识发现的核心环节，是信息科学技术发展的必然结果。其中的支持向量机技术是在统计学习理论框架下提出的一种新的学习机器，其完备的理论基础和优良的推广性能，为水雷兵器引信技术的智能化指引了一个很有发展潜力的方向。本文的研究目的在于应用数据挖掘技术实现三类水中目标的分类识别，主要研究内容与创新如下：
1．分析了舰船辐射噪声的通过特性、调制特性、谱特性与线谱的特征及应用，特别分析了辐射噪声谱的“三非”特性；研究了基于高阶谱分析和小波变换的舰船辐射噪声特征。
2．提出了高阶量1(1/2)维谱和小波变换交互分析进行特征提取的方法，比较好地反映了舰船辐射噪声的非线性、非平稳和非高斯特性。实验结果表明：优化的特征集可以有效地增强类内紧密性和类间可分性。
3．在研究了数据挖掘、支持向量机及其有关技术的基础上，建立了实现三类水中目标识别的SVM方法；采用线性规划SVM解决了传统二次规划SVM在海量样本情况下导致的时间和空间复杂度问题；提出了将最近邻分类与支持向量机分类相结合的SVM-KNN分类器应用于水中目标识别的思想，较好地解决了应用支持向量机分类时核函数参数的选择问题，取得了更高的分类准确率。
4．文中的三类实船样本分类实验证明了SVM方法的有效性和较之以往方法的优越性，取得了较好的识别结果，对水中目标识别和水雷引信技术的发展有一定的促进作用。
As a promising and flourishing frontier of the Information Technology, Data Mining acts as the essential procedure in knowledge discovery in database (KDD). And the Support Vector Machine (SVM) is a new kind of learning machine, which is based on the Statistical Learning Theory. Its complete theory and excellent performance make a potential future for mine intelligence. The aim of this thesis is to realize 3-class underwater targets' recognition by means of Data Mining technique. The main work and originality in this thesis can be summarized as following:
1. Analysis of acoustic characteristics of ship's radiated noise both in time and frequency domain, especially its deviation from the ordinary statistical signal. Studies on two modern signal processing methods-Higher-Order Statistics (HOS) and wavelet transform.
2. A novel signal processing method based on alternate feature optimization is introduced and analyzed in this thesis. And a new underwater target recognition system using the optimized feature and SVM is presented here. The system utilizes the alternate feature extraction method to optimize the feature selection process.
3. The optimized feature set feeds a 3-class classification module, which is based on the traditional binary SVM classifier. And the proposed linear programming SVM reduces the burden of the SVM classifier and improves its learning speed and classification accuracy. A new algorithm that combined SVM with K Nearest neighbor (KNN) is presented and it comes into being a new classifier, which can not only improve the accuracy compared to sole SVM, but also better solve the problem of selecting the parameter of kernel function for SVM.
4. The numerical experiments show that the proposed data mining system based on SVM can achieve effective results in underwater targets classification.
1．分析了舰船辐射噪声的通过特性、调制特性、谱特性与线谱的特征及应用，特别分析了辐射噪声谱的“三非”特性；研究了基于高阶谱分析和小波变换的舰船辐射噪声特征。
2．提出了高阶量1(1/2)维谱和小波变换交互分析进行特征提取的方法，比较好地反映了舰船辐射噪声的非线性、非平稳和非高斯特性。实验结果表明：优化的特征集可以有效地增强类内紧密性和类间可分性。
3．在研究了数据挖掘、支持向量机及其有关技术的基础上，建立了实现三类水中目标识别的SVM方法；采用线性规划SVM解决了传统二次规划SVM在海量样本情况下导致的时间和空间复杂度问题；提出了将最近邻分类与支持向量机分类相结合的SVM-KNN分类器应用于水中目标识别的思想，较好地解决了应用支持向量机分类时核函数参数的选择问题，取得了更高的分类准确率。
4．文中的三类实船样本分类实验证明了SVM方法的有效性和较之以往方法的优越性，取得了较好的识别结果，对水中目标识别和水雷引信技术的发展有一定的促进作用。
As a promising and flourishing frontier of the Information Technology, Data Mining acts as the essential procedure in knowledge discovery in database (KDD). And the Support Vector Machine (SVM) is a new kind of learning machine, which is based on the Statistical Learning Theory. Its complete theory and excellent performance make a potential future for mine intelligence. The aim of this thesis is to realize 3-class underwater targets' recognition by means of Data Mining technique. The main work and originality in this thesis can be summarized as following:
1. Analysis of acoustic characteristics of ship's radiated noise both in time and frequency domain, especially its deviation from the ordinary statistical signal. Studies on two modern signal processing methods-Higher-Order Statistics (HOS) and wavelet transform.
2. A novel signal processing method based on alternate feature optimization is introduced and analyzed in this thesis. And a new underwater target recognition system using the optimized feature and SVM is presented here. The system utilizes the alternate feature extraction method to optimize the feature selection process.
3. The optimized feature set feeds a 3-class classification module, which is based on the traditional binary SVM classifier. And the proposed linear programming SVM reduces the burden of the SVM classifier and improves its learning speed and classification accuracy. A new algorithm that combined SVM with K Nearest neighbor (KNN) is presented and it comes into being a new classifier, which can not only improve the accuracy compared to sole SVM, but also better solve the problem of selecting the parameter of kernel function for SVM.
4. The numerical experiments show that the proposed data mining system based on SVM can achieve effective results in underwater targets classification.
引文
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