基于 Volterra 级数的自适应水声信号预测方法研究

doi:10.3969/j.issn.1000-1093.2013.09.019

兵工学报 ›› 2013, Vol. 34 ›› Issue (9): 1173-1179.doi: 10.3969/j.issn.1000-1093.2013.09.019

基于 Volterra 级数的自适应水声信号预测方法研究

房媛媛_1,2, 李亚安₁, 崔琳₁, 尚进₁

1. 西北工业大学航海学院, 陕西西安710072; 2. 天津航海仪器研究所, 天津300131

收稿日期:2012-07-08 修回日期:2012-07-08 上线日期:2013-11-11
作者简介:房媛媛(1987—),女,硕士研究生。
基金资助:
国家自然科学基金项目(51179157)

Maneuvering-Deceleration Guidance Algorithm Based on Atmosphere Estimation for Reentry Vehicle

FANG Yuan-yuan_1,2, LI Ya-an₁, CUI Lin₁, SHANG Jin₁

1. College of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072,Shaanxi,China; 2. Tianjin Navigation Instrument Research Institute,Tianjin 300131, China

Received:2012-07-08 Revised:2012-07-08 Online:2013-11-11

摘要/Abstract

摘要： 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。

关键词: 声学, 水声信号, Volterra 自适应滤波器, 多步预测

Abstract: 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 underwater acoustic signal processing. According to Volterra series theory, a nonlinear dynamic model of underwater acoustic signal is established to realize the noise reduction of background noise and the suppression 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, respectively, by using the two-order Volterra adaptive filter and the singular value decomposition adaptive algorithm. The simulation results show that the iterative method has higher prediction performance and effec- tive multi-step prediction capability.

Key words: acoustics, underwater acoustic signal, Volterra adaptive filter, multi-step prediction

中图分类号:

TB56

房媛媛, 李亚安, 崔琳, 尚进. 基于 Volterra 级数的自适应水声信号预测方法研究[J]. 兵工学报, 2013, 34(9): 1173-1179.

FANG Yuan-yuan, LI Ya-an, CUI Lin, SHANG Jin. Maneuvering-Deceleration Guidance Algorithm Based on Atmosphere Estimation for Reentry Vehicle[J]. Acta Armamentarii, 2013, 34(9): 1173-1179.

参考文献

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基于 Volterra 级数的自适应水声信号预测方法研究

Maneuvering-Deceleration Guidance Algorithm Based on Atmosphere Estimation for Reentry Vehicle

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