基于神经网络和散射中心模型的目标参数提取

从雷达回波中获取目标几何参数信息往往存在高计算成本、非线性等困难。该文基于卷积神经网络和前馈神经网络, 提出了一种依据散射中心时频像特征的目标类型自动识别和目标几何参数自动提取方法。由于构建一个神经网络需要大量的训练数据样本, 而扩展目标的散射场计算又非常耗时, 利用基于已知目标已建立的散射中心模型, 快速生成大样本训练数据, 有效解决了训练样本难以获得的问题。以弹头类目标为例给出了数值实验结果, 证实了所提方法的有效性。

Target parameter extraction based on neural network and scattering center model

Target geometry extraction from radar echoes are often subject to high computational cost, non-linearity, and other difficulties. In this paper, based on convolutional neural network and back propagation neural network, a method is proposed to automatically identify the target pattern and extract the target geometry parameters from the time-frequency image characteristics of scattering center. Since the construction of a neural network requires a large number of training data samples, and the computation of the scattering field of the extended target is very time-consuming, the scattering center model established based on the known target is used in this paper to quickly generate large sample training data, which effectively solves the problem of obtaining training samples. Taking warhead targets as an example, the neural networks are established, and the effectiveness of the proposed method is verified by numerical experiment results.