中文摘要：

针对目前基于随机信号模型的海上目标图像海杂波抑制的常用方法效果不甚理想，提出了一种基于混沌神经网络的海上目标图像海杂波抑制方法。考虑到海杂波运动固有的混沌性导致其海上目标图像具有混沌特征，在海杂波混沌动力系统相空间重构的基础上构造海杂波动力学模型，运用径向基函数（RBF）神经网络提取模型参数，以此预测和抑制海杂波。用实际海上目标图像进行海杂波抑制实验，并与最小均方（LMs）算法和最大Lyapunov指数法相比，实验结果表明，本文方法对海杂波具有良好的抑制效果，使其平均绝对误差（MAD）减小了30％，信噪比（SNR）提高了4到6dB，可为海上弱小目标检测提供新的解决思路。

英文摘要：

The methods based on statistical model are usually used to suppress sea clutter and detect tar- get by photoelectric imaging system, but their performance of target detection is generally unstable in the situation of high ocean wave especially. A novel sea clutter cancellation approach for target image at sea based on chaotic neural network is proposed in this paper. The motion of sea clutter is chaotic, which in- duces that the photoelectric image of sea clutter is also chaotic. The dynamic model of sea clutter is con- structed after the sea clutter phase space of chaotic dynamical system is reconstructed. The model pa- rameters used to predict sea clutter are obtained by radial basis function （RBF） neural network. Then the sea clutter suppression can be performed by clutter cancellation between the predicted and real sea clutters. The real sea clutter photoelectric image is used to test the model, and the results show that this method is more effective to suppress sea clutter compared with the least mean square and the largest Lyapunov exponent methods,the mean deviation is reduced by 30%, and the signal-to-noise ratio is in- creased by 4- 6 dB. At the same time, a novel strategy based on chaos theory will be adopted to improve the performance of dim target detection in sea clutter by the photoelectric imaging system.