水下运动目标时变噪声场欠定盲提取模型及其算法研究

项目名称： 水下运动目标时变噪声场欠定盲提取模型及其算法研究

项目编号： No.51265018

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 机械、仪表工业

项目作者： 伍星

作者单位： 昆明理工大学

项目金额： 50万元

中文摘要： 声源特征识别是针对水下运动目标的一个重大研究课题，盲信号处理技术为水下声源识别提供了一个新的途径。针对水下声场的强干扰、多途效应及时变特性，以盲解卷积模型及算法为基础，建立水下运动目标时变噪声场欠定盲提取模型，进而从工程信号处理角度出发研究其关键理论及算法:①采用差分进化理论研究声源贡献率，优化声源数目，利用广义数学形态滤波等前处理手段消弱干扰噪声，根据机械结构参数构建参考信号以凸显主要声源特征，降低大数目声源及时变声场对分离精度的影响；②通过建立压缩感知与欠定盲解卷积的等价关系，针对水下声信号提取优化压缩感知框架，在此框架下利用Ｋ均值奇异值分解等稀疏分量分析方法训练稀疏字典，使用追踪算法计算得到稀疏分量，结合优化两步法进行盲源分离。③以鱼雷实体为研究对象，在各种实验条件下进行鱼雷辐射噪声盲提取研究，分离并评估鱼雷主要噪声源的特性和量级，为鱼雷减振降噪及其隐身性能的改进提供科学依据。

中文关键词： 欠定盲解卷积；广义形态滤波；欠定源估计；压缩感知；机械故障诊断

英文摘要： Sound source characteristics identification is an urgent topic for researches which aming at underwater moving target, blind signal pprocessing (BSP) technology now becomes a powerful tool in the field of under water sound source identification. This research, which bases on blind denconvolution (BD) and other signal processing theories, mainly focuses on strong interferance, multi-path effect and time variant characteristics in underwater sound fields. Key theories and algorithms in underwater moving target undetermined blind extraction are studied: 1. Firstly, differential evolution is used for sound source contribution rate estamation, generalized mathematical morphological filter and other pretreatment methods are applied to filtering out the interference noises, reference signals is built to make the main sound source characteristics more obviously and decrease the effect brought by large number of sources and time-varying sound field, 2. Then, the equivalence between the problem of estimating the source in undetermined blind denconvolution and the compressed sensing (CS) is analyzed and the framework of CS is built, sparse component analysis (SCA) methods such as K-means singular value decomposition (K-SVD) are used to train sparse dictionary self-adaptive under this framewaork. 3. Finally, the sparse comp

英文关键词： Undetermined Blind Deconvolution；Generalized Mathematical；Undetermined source estimation；Compressed Sensing；Mechanical fault diagnosis