【作者】 王军；

【导师】 李亚安；

【作者基本信息】 西北工业大学 ， 水声工程， 2004， 硕士

【摘要】 水下目标回波信号是典型的非平稳信号，时频分析是分析非平稳信号的有效工具。当回波信号为多分量信号时，时频分布中含有大量的交叉项。交叉项的存在，一方面对信号处理的结果产生干扰，不利于回波信号的特征提取。因此，抑制交叉项一直是时频分析研究领域一个十分棘手的问题，同时也是时频分析得以更广泛应用于各信号处理领域所要克服的网难之一。另一方面，交叉项反映了信号成分之间的相干程度，在某些情况下，信号成分之间的这种相干性反而有利于信号的检测。本文以时频分析中的交叉项为主线，系统的研究了交叉项在水下目标回波信号处理中的应用。 本文的研究内容与创新如下： 1．系统的研究了多分量信号Wigner-Ville分布中的交叉项，根据交叉项在模糊域的特点，提出了基于信号的自适应高斯核时频分布。同时，考虑到自适应高斯核时频分布不利于分析信号的细节以及实时在线处理等特点，本文提出一种改进算法，即用随时间不断变化的自适应短时高斯核代替基于整个信号的整体自适应高斯核，从而得到一种性能更加优良的自适应短时核时频分布。通过对仿真信号计算其自适应短时高斯核时频分布，仿真结果表明，无论从改善信号的时频分辨率还是抑制交叉项方面，自适应短时核时频分布均表现出了较好的特性。 2．研究了Wigner-Hough变换在抑制交叉项中的应用。通过对Wigner-Hough变换作一定的改进，提出了基于信号自适应高斯核时频分布的Hough变换。仿真结果表明，改进算法不仅可以很好的抑制交叉项，而且在信噪比很低时仍具有很好的性能。 3．首次利用Wigner-Ville分布中的交叉项并结合人工神经网络，提出了一种新的基于模块学习策略的信号检测方法，该方法对淹没在非平稳海洋环境噪声中的目标信号可进行有效地检测，并且这种学习策略没有对环境噪声作任何假设。仿真结果表明，与传统的基于匹配滤波器的检测方法相比，新方法具有很大的优越性。更多还原

【Abstract】 Underwater target echoes are typically nonstationary signals and Time-Frequency analysis is effective to deal with nonstationary signals. When the echoes have multi-components there are much cross-components in the Time-Frequency distribution (TFD), so it is not suitable for the feature extraction of the echoes. Cross-components suppression is always the intractability in the studies of TFD and the difficulty in signal processing we should overcome. On the other hand, the cross-components have much information of two (or more) signal components, so it is suitable for signal detection. This paper studies mainly on the cross-components in the underwater target echoes signal processing.The main work and originality in this paper can be summarized as below: 1.Studies on the cross-components in the Wigner-Ville distribution of multi-component signals. We present Adaptive Gaussian Kernel Time-Frequency distribution according to the feature of cross-components in the ambiguity domain. But it is not suitable for online implementation or for tracking signal components with characteristic that change with time, a new modified algorithm is present based on the adaptive short-time kernel. Simulations show that adaptive short-time kernel time-frequency distribution has better performance than anyother time-frequency distribution not only in the time-frequency resolution but also in the cross-components suppression.2.Studies on the Wigner-Hough transform in the suppression of cross-components, a modified algorithm is present based on Adaptive Gaussian Kernel Time-Frequency distribution, this algorithm not only suppresses the cross-components but also has good performance in the low SNR.3. A novel modular learning strategy is present based on the cross-component in the WVD and neural network for the detection of a target signal of interest in a nonstationary oceanic environmemt and this strategy makes no assumptions on the environmemt. Simulations show that this new method has much superiority over matched filter.更多还原