海杂波的短时非线性预测研究

海杂波预测是雷达信号处理和目标检测的研究热点。在海杂波具有混沌特性和非线性非平稳特点的基础上，研究了基于归一化RBF神经网络和最小二乘支持向量机（LSSVM）两种方法对海杂波时间序列进行非线性预测。考虑到海杂波是来自于移动海面的回波，预测应该考虑空间信息，因此提出一种基于LSSVM-耦合映像格子（CML）的海杂波时空预测，这样预测更具有物理意义。以实测海杂波数据作为预测的初始数据和预测效果比对，采用均方差和最大绝对误差作为预测效果评价标准。实验结果表明，由于LSSVM-CMI，算法考虑了海杂坡的时空信息，预测效果最优。

Nonlinear Prediction of Short-Time Sea Clutter

Sea clutter prediction is the key point in the research on radar signal processing and target detection.As the fact that sea clutter has chaotic characteristic and is nonlinear and nonstationary.Normalized RBF neural network and least squares support vector machines(LSSVM) are applied to nonlinear prediction of sea clutter time series.Considering sea clutter being the backscatter of the moving sea surface,so the spatial information should be considered for prediction.A new method of LSSVM-CML(Coupled Map La...