基于数据分段互双谱的海水深度计算方法

海水深度数据是开展OBC水、陆检资料合并处理的基础。结合时间序列高阶统计量提出了一种基于数据分段互双谱法的海水深度反演方法。通过对分段后的数据计算上、下行波场数据自双谱和互双谱,再计算相干互双谱和互相关谱,最后计算海水深度。水陆检数据分段计算,利用了叠加特性,增加了方法抗高斯噪声的能力。高阶统计量的互双谱和相干互双谱,具有压制高斯与非高斯有色噪声的优点。利用本文方法对OBC数据进行海水深度计算,结果与实测海水深度较吻合。计算的海水深度用于双检数据合并,压制海水鸣震干扰效果明显,为后续速度模型建立和偏移成像处理奠定了良好的基础。     

Second-order spectra for quadratic nonlinear systems by Volterra functional series: Analytical description and numerical simulation

Higher-order spectral analysis techniques are often used to identify nonlinearities in complex dynamical systems. More specifically, the auto- and cross-bispectrum have proven to be useful tools in testing for the presence of quadratic nonlinearities based on knowledge of a system's input and output. In this paper, analytical expressions for the auto- and cross-bispectrum are developed using a Volterra functional approach under the assumption of a zero-mean, stationary Gaussian input; proper simplifications are presented when the whiteness of the input signal is also imposed. These formulae show the contributions of the bispectrum in terms of the system frequency response function and elementary physical properties of the system. Simulations based on a stochastic numerical integration technique accompany the analytical solutions for a mechanical mass–spring–damper system possessing quadratic damping and stiffness coefficients and subjected to Gaussian white noise excitation. Subsequent estimates of the bispectrum based on the simulated signals show excellent agreement with theory. These results show how modes may interact nonlinearly producing intermodulation components at the sum and/or difference frequency of the fundamental modes of oscillation. The presence and extent of nonlinear interactions between frequency components are identified. Advantages of using higher-order spectra techniques will be revealed and pertinent conclusions will be outlined.     

声矢量信号双谱与互双谱估计算法

提出了声矢量信号双谱与互双谱估计算法,给出了算法的具体步骤。将算法应用于两类水中目标的特征提取,并用所提取特征构造了LMBP神经网络的输入向量集,对矢量水听器实测的水中目标进行了分类识别。识别结果验证了所提出算法的有效性。实验表明,B类目标识别率优于A类目标,原因是由于B类目标特征频率较集中,而A类目标特征频率较分散所致。互双谱特征分类结果优于双谱特征分类结果这个事实是与声压振速联合信号处理优于声压或振速单一信号处理相吻合的。