海底混响中基于前后向预测模型的信号检测

提出了一种基于径向基函数神经网络的具有更高建模精度的前后向预测模型,并用该模型对海底混响时间序列进行建模,以模型对于声纳接收信号的一步预测误差作为检验统计量,检测海底混响中的微弱目标回波信号.对湖试单频波束数据的处理结果表明,前后向预测模型对于波束信号包络的归一化预测误差比前向预测模型小约1个数量级,两种预测模型对于波束信号包络的预测误差都可以用作检验统计量,较好地检测出混响中的目标回波.

Forward and Backward Prediction Model Based Signal Detection in Sea Bottom Reverberation

A forward and backward prediction model with higher accuracy based on radial basis functions(RBF) neural networks was proposed and used to model sea bottom reverberation.The one-step prediction error of the prediction models was used as the statistics for weak echoes detection.The results for envelopes of single-frequency reverberation in lake-trails show that,the defined prediction error of the forward and backward prediction model for that is about 10 lower than that of the forward prediction model,and the prediction error of both prediction models can be used as statistics for the detection of weak echoes in reverberation.