基于混沌理论的海底混响中目标回波提取(英文)

Echo extraction from bottom reverberation based on chaos

提出了基于混沌理论的混响中目标回波提取新方法。该方法主要得益于一种新的预测模型,该模型基于径向基函数神经网络,综合利用了时间序列的前向和后向预测,解释了该模型用于混沌信号分离的基本原理,用几种混沌时间序列分析了该模型用于混沌信号建模和谐波信号提取的性能。该方法用于湖试混响中目标回波提取的结果表明:该模型可以用于提取信混比不小于1dB的目标回波。

A novel method based on chaos theory for echo extraction from reverberation is proposed. Effectiveness of this method is mainly due to a new prediction model based on radial basis function （RBF） neural networks, which uses forward and backward prediction （FBP）. Principles of the model used for chaotic signal separation is explained. Performances for the chaotic signal modeling and harmonic signal extraction are analyzed using several chaotic time series as exampies. The result of the model in the extraction of object echoes from real lake-bottom reverberation shows that the model can be used to extract object echoes when signal-to-reverberation-ratio is greater than ldB.