水声信号的非线性建模与预测方法

对水声信号尤其是混响干扰和背景噪声的预测和滤波,是水下目标信号检测的基础,在非平稳、非高斯、非线性水声信号处理中具有重要应用.本文利用最小二乘估计和Volterra级数理论,分别对水声信号建立预测模型并进行一步及多步预测,通过分析预测结果,选取最优预测参数.仿真结果表明,基于奇异值分解的Volterra级数模型的预测相对误差较最小二乘估计小一个数量级,预测结果更加逼近真实值.

Nonlinear Modeling and Prediction of Underwater Acoustic Signal

The prediction and filtering of underwater acoustic signals, especially of reverberation interference and back- ground noise, lay the foundation of underwater target signal detection and processing of non-stationary, non-Gaussian and nonlinear underwater acoustic signals. In this paper, based on the linear theory of least square estimation and the Volterra series theory, two prediction models of target signal are established to conduct one-step and multi-step predictions. Thus, the optimal prediction parameters are obtained by comparing and analyzing the prediction results. Simulations show that the prediction model based on singular value decomposition of Volterra series achieves more accurate results in predicting the underwater acoustic signal, and its relative error of prediction is one-order of magnitude smaller, compared with the model based on least square estimation.