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为对海杂波进行准确预测,根据海杂波具有的非线性不确定性,应用线性和非线性预测理论建立预测模型.针对logistic混沌映射信号和IPIX实际海杂波数据背景下的弱目标,分别采取基于自回归(AR)的线性模型、基于径向基神经网络(RBF)和Volterra级数滤波器的非线性模型进行预测.实验结果表明:非线性预测模型更适合于混沌背景下,因其目标和杂波的预测误差相差较大,可采取非线性预测并设置门限的方法进行目标检测;对于IPIX雷达数据,其混沌特性较logistic弱,目标和杂波的预测结果相差不大,可采用似然比检测方法.
In order to accurately predict the sea clutter and predict the nonlinear clutter based on the nonlinear uncertainties of the sea clutter, linear and nonlinear prediction theories are used to establish the prediction model.According to the weak targets of the logistic chaotic map signal and IPIX real sea clutter data, The nonlinear models based on Radial Basis Function Neural Network (RBF) and Volterra series filter are respectively used to predict the nonlinear model based on the autoregressive (AR) model.The experimental results show that the nonlinear model is more suitable for the chaotic The target and clutter prediction errors are quite different. Nonlinear prediction and threshold setting can be used for target detection. For the IPIX radar data, its chaotic characteristics are weaker than those of logistic, and the prediction results of target and clutter are similar. Likelihood ratio test method is used.