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针对浅海环境中低频宽带水声脉冲信号, 研究基于频散特征结合时频分析的单水听器距离和深度估计方法. 以简正波理论为依据, 将单水听器上的接收信号表示成一系列传播模式之和的形式, 分析了经典波导环境下的频散现象, 采用自适应径向高斯核函数的时频分析方法来表征接收信号的频散特征. 为提高时频分辨率, 采用自适应径向高斯核函数的时频分布来提取频散关系曲线中传播模式的到达时间差, 利用模式的到达时间差估计声源的距离. 采用多模式联合匹配的方式, 通过二值掩模滤波的时频滤波方法, 提取所需的模式. 通过计算实际提取出的模式能量与预测的模式能量之间的误差, 建立代价函数, 并通过模式能量匹配的方式, 确定声源的深度. 通过对基于Pekeris波导模型的浅海环境进行仿真验证, 结果表明: 自适应径向高斯核函数的时频分析方法能够很好地反映信号本身的频散特征, 具有较高的时频分辨率, 克服了传统短时傅里叶变换时频表征的限制, 使得模式在时频域更加容易辨识和分离; 从测距效果来看, 不同模式组合下的距离估计结果不同, 采用在时频面上具有较高能量的模式, 可得到较为准确的距离估计; 选用高能量的模式所得的距离估计的相对误差小于2%. 在定深方面, 参与联合匹配的模式个数越多, 代价函数的峰值更加地尖锐, 同时具有低的伪峰, 深度估计的性能会进一步有所提升. 该工作对于研究低频水声脉冲信号的分离和提取具有重大意义.A method of range and depth estimation was studied using a single hydrophone based on the dispersive characteristic and time-frequency analysis for low frequency underwater acoustic pulse signals in shallow water environment. First, the signal received on a single hydrophone can be decomposed into a series of modes within the frame work of normal mode theory, and then the dispersive characteristic of the propagating modes can be analyzed using the time-frequency analysis. In order to improve the time-frequency resolution, the use of the time-frequency distribution with adaptive radial-Gaussian kernel extracts the arrival time difference of propagating modes in dispersion curve, which can be used to estimate source range. Mode energy can be extracted using binary time-frequency mask filtering based on multi-mode joint matching processing; and the source depth can be estimated by comparing the differences of the mode energy of the real data and simulated replica data, yielding a contrast function. Simulation results from a shallow-water Pekeris waveguide show that the time-frequency distribution with adaptive radial-Gaussian kernel represents well the dispersion characteristics of the underwater acoustic pulse signals, provides higher time-frequency resolution and overcomes the problem of the inherent limit for the time resolution and frequency resolution in the traditional short-time Fourier transform, so that the modes can be separated and identified more easily in the time-frequency plane. From the result of the range estimation, the different mode combinations have different results of the range estimation. The range estimation result can be obtained accurately by using the mode with high energy in the time-frequency plane. The relative error in range estimation is less than 2% by using the mode with high energy. In terms of the depth estimation, the more the number of joint matching mode, the more sharp peak and low fake peaks the contrast function has, so that the depth estimation is further improved by incorporating more modes. This research has great significance for studying the extraction and separation of low frequency underwater acoustic pulse signals.
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