声纳中的空时联合处理方法研究
文献类型：学位论文
| 作者 | 李维 |
| 学位类别 | 博士 |
| 答辩日期 | 2009-05-27 |
| 授予单位 | 中国科学院声学研究所 |
| 授予地点 | 声学研究所 |
| 关键词 | 空时自适应信号处理 声纳 混响 检测 估计 |
| 其他题名 | On Space Time Adaptive Processing for Sonar |
| 学位专业 | 信号与信息处理 |
| 中文摘要 | 声纳是利用水下声波进行目标探测和定位以及水下通信的综合性电子设备，声纳信号处理技术是影响其系统性能的重要因素。空时自适应处理是雷达上一种主要用来抑制空时散射的杂波的二维的自适应滤波处理。鉴于声纳与雷达的相似性，将空时联合处理应用于水声信号处理是一种必然趋势。然而由于水声信道的复杂特性，空时自适应处理很难直接应用于水声信号处理。本文针对于水声信号的特点，对适用于声纳的空时自适应处理相关算法进行了理论研究与实验分析。 论文贡献主要体现在以下几个方面： 1. 提出一种基于二维自回归模型的新型空时自适应预白化处理器，并针对该处理器提出相应的检测器。该方法假定混响背景的时间局部平稳性，对信号进行分段处理。根据当前段的混响数据对下一段的混响进行白化处理。将传统的应用于单通道的AR处理器推广到多通道，实现空域时域同时进对混响进行抑制，经比较，检测效果提高10-12dB。 2. 提出了一种新型信号子空间混响抑制方法，该方法是在主分量反演(Principal Component Inverse, PCI)方法的基础上，将Eckart and Young理论再一次应用于信号前向矩阵，大大提高了单通道混响抑制的能力，同时提出分块前向矩阵应用于混响阵列信号，并将新的信号子空间抑制方法应用于分块前向矩阵，实现空时自适应预白化处理器。真实数据实验和仿真实验验证了新方法的有效性。 3. 基于目前对于海洋混响混沌属性的分析，提出了一种基于高阶小脑模型控制器(Cerebellar Model Articulation Controller, CMAC)的混响预测器，并将其应用于混响抑制来实现混响中的信号检测。接着，基于空时联合处理思想，结合耦合映射格点(Coupled Map Lattice, CML)的方法，提出了空时联合处理的神经网络混响预测器用于检测。实验将提出的高阶CMAC混响中的信号检测算法与原有的使用径向基函数(Radial Basis Function, RBF)神经网络模型的混响中信号检测算法进行性能分析与比较，高阶CMAC方法大大提高了运算效率，而借助了CML的空时联合处理算法具有更强检测性能。 4. 针对声纳中的目标参数估计的问题，在匹配场处理的几种常见方法的基础上，提出了一种可变系数熵的方法，同时将声场传播特性与空时自适应处理相结合，介绍了空时匹配场处理算法，并提出了一种适用于空时匹配场处理的可变系数熵的方法。计算机仿真实验分别将新提出的方法与几种匹配场处理方法进行比较分析，验证新提出的空时匹配场处理方法——可变系数熵方法对目标的方位，距离以及速度的估计的可行性，从而提供了新的空时匹配场处理的方法。 |
| 英文摘要 | Sonar is a kind of integrated electronic equipment. It can be used to achieve targets detection or location and underwater communication with sound waves. As the principal components，sonar signal processing technologies may affect the performance of the whole sonar system greatly. Space Time Adaptive Processing(STAP) is a two-dimentional adaptive filter mainly to suppress douply spread clutter in Radar. Basing on the analogy of Sonar and Radar, taking STAP into underwater signal processing is a certain. However, it is difficult to use STAP directly in underwater signal processing since the complexity of underwater channel. This dissertation focuses on the unique of the underwater environment, and is devoted to the theoretical research and experimental analysis on space time adaptive processing for Sonar. The main contributions of the dissertation are as follows: 1. A space time adaptive prewhitener for reverberation based on a two-dimensional autoregressive model is proposed. The space time adaptive prewhitener jointly processes received data in angle and Doppler to improve the separation of a target from reverberation. It is more effective than the traditional methods which process data in angle and Doppler separately. The detector using the space time adaptive prewhitener is shown to yield a gain of 10–12 dB in detection than previously one dimension autoregressive prewhitener when operating in a reverberation background containing target echoes. 2. A new algorithm called Signal Subspace Extraction (SSE) for detecting and estimating the target echo in reverberation is presented. The new algorithm can be viewed as an extension of the Principal Component Inverse (PCI) and maintains the benefit of PCI algorithm, moreover shows better performance due to a more reasonable reverberation model. In the SSE approach, a best low-rank estimate of a target echo is extracted by decomposing the returns into short duration subintervals and by invoking the Eckart-Young Theorem twice. Furthermore, a block forward matrix is proposed to extend the algorithm to the space-time array problem. The comparison between the block forward matrix and the conventional matrix is discussed. The new algorithm also allows echo separation. Examples are presented using both real, active-sonar data and simulated data. 3. A novel method based on chaos theory for echo extraction from reverberation is proposed．Effectiveness of this method is mainly due to a new prediction model based on a high order Cerebellar Model Articulation Controller(CMAC) neural networks. Principles of the model used for chaotic signal separation is explained. Then following the STAP idea, a space time reverberation predicter is presented which conbines the high order CMAC and Coupled Map Lattice(CML). The result of the model in the extraction of object echoes from real reverberation shows that the model can be used to extract object echoes. Also, the comparation among Radial Basis Function(RBF) method, high order CMAC and CMAC is presented. 4. To effectively estimate parameters of targets in sonar system, basing on several common methods in Matched-Field Processing(MFP), a VCEM algorithm is proposed. Then, by combining sound propagation model and space time adaptive method, Space Time Adaptive Matched-Field Processing(STAMFP) is introduced. And a new STAMFP method, VCTEM algorithm is presented. Experiments indicate the advantages of the new algorithms. The new algorithms can provide higher beamforming gain than the original algorithm both in time series and space time data. |
| 语种 | 中文 |
| 公开日期 | 2011-05-07 |
| 页码 | 118 |
| 源URL | [http://159.226.59.140/handle/311008/522]  |
| 专题 | 声学研究所_声学所博硕士学位论文_1981-2009博硕士学位论文 |
| 推荐引用方式 GB/T 7714 | 李维. 声纳中的空时联合处理方法研究[D]. 声学研究所. 中国科学院声学研究所. 2009. |
入库方式： OAI收割
来源：声学研究所
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