海洋混响的非线性预测模型及其应用研究
文献类型：学位论文
| 作者 | 甘维明 |
| 学位类别 | 博士 |
| 答辩日期 | 2008-05-29 |
| 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 | 海洋混响 相空间重构 非线性 预测模型 自适应滤波 Volterra级数 神经网络 信号检测 |
| 其他题名 | Study on the Nonlinear Prediction Model of Ocean Reverberation and Its Application |
| 学位专业 | 声学 |
| 中文摘要 | 本文在对海洋混响时间序列进行相空间重构的基础上，构建海洋混响的非线性预测模型，然后通过在不同海域进行的两次海上实验获取的混响信号，对预测模型进行检验，并寻求预测模型在海洋混响背景下目标信号检测中的应用。文中用于预测海洋混响的模型有三个：自适应非线性滤波方法、二阶Volterra级数展开法以及BP神经网络。 本文提出一个自适应非线性滤波方法，作为混响的预测模型，通过海上实验的混响数据对方法进行检验，结果表明，只需很少的训练样本，该方法就可以对海洋混响信号作出很好的预测。利用目标信号与混响之间非线性动力学特征存在的差异，将该预测模型应用于海洋混响背景下目标回波的检测，以模型的预测误差作为检验统计量来检测混响中的目标回波，可以在一定程度上抑制混响干扰，并通过混响实验数据中含有目标回波的混响序列验证了这一点。文中还将二阶Volterra级数展开法应用到海洋混响信号的预测，并与自适应非线性滤波方法进行比较，自适应非线性滤波预测方法要优于二阶Volterra级数展开法。 本文构造BP神经网络模型用于海洋混响信号的预测，可以很好地预测海洋混响信号，将它应用于海洋混响背景下目标信号的检测时，可以获得很好的检测效果，达到抑制混响的目的。 |
| 英文摘要 | Nonlinear prediction models are proposed to predict ocean reverberation signals based on the phase space reconstruction of reverberation time series. The models are tested by the experimental reverberation signals measured in different sea areas and applied to the detection of target signals in ocean reverberation. Three models, i.e. adaptive nonlinear filter, the second-order Volterra series expansion and BP neural network, are introduced. The proposed adaptive nonlinear filter prediction algorithm of ocean reverberation is testified by the experimental reverberation data. The results show that the model is fit for the prediction of ocean reverberation and only a few samples are needed to train the algorithm. The model can be applied to the detection of the target signal in the reverberation, in virtue of the difference of nonlinear characteristics between the target echo and the reverberation signal. If the errors between the predicted and experimental data are considered as the statistical test variable to detect the target in the reverberation series, the reverberation is suppressed. This is verified by the experimental reverberation data containing an echo. The second-order Volterra series expansion algorithm is also considered as the prediction model of ocean reverberation and compared with the adaptive nonlinear filter prediction algorithm. And the adaptive nonlinear filter prediction algorithm is better than the second-order Volterra series expansion algorithm. BP neural network is constructed to predict the ocean reverberation and works very well. The detection performance for the target signal in the reverberation is good and the reverberation is suppressed. |
| 语种 | 中文 |
| 公开日期 | 2011-05-07 |
| 页码 | 98 |
| 源URL | [http://159.226.59.140/handle/311008/282]  |
| 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 | 甘维明. 海洋混响的非线性预测模型及其应用研究[D]. 声学研究所. 中国科学院声学研究所. 2008. |
入库方式： OAI收割
来源：声学研究所
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