基于Volterra级数的自适应水声信号预测方法研究

Research on Prediction of Underwater Acoustic Signals Based on Volterra Adaptive Filter

背景噪声和混响干扰是声纳目标探测中的主要干扰源,如何有效地减小它们对声纳工作特性的影响一直是水声信号处理关注的焦点。利用Volterra级数理论,建立水声信号的非线性动力学模型,通过对水声信号的局部预测,实现对背景噪声的降噪和混响干扰的抑制。结合二阶Volterra自适应滤波器和基于奇异值分解的自适应滤波算法,分别采用直接法和迭代法完成了对水声信号的一步及多步预测。仿真结果表明,基于Volterra级数模型的水声信号迭代预测方法比直接法不仅具有更好的预测性能,而且还可以实现多步预测。

Background noise and reverberation interference are the main interference sources in the sonar target detection. How to reduce their effects on sonar performance effectively has been a focus of under- water acoustic signal processing. According to Volterra series theory, a nonlinear dynamic model of un- derwater acoustic signal is established to realize the noise reduction of background noise and the suppres- sion of reverberation interference by predicting the underwater acoustic signal. The direct and iterative methods are used for the one-step and multi-step predictions of the underwater acoustic signal, respective- ly, by using the two-order Volterra adaptive filter and the singular value decomposition adaptive algo- rithm. The simulation results show that the iterative method has higher prediction performance and effec- tive multi-step prediction capability.