基于CNN-LSTM神经网络的前视成像算法

雷达前视成像作为雷达成像领域的难点与重点，在自动驾驶、导航、精确制导等方面具有广阔的应用前景。传统的前视成像算法受限于天线孔径的宽度，无法实现高分辨率的成像，本文使用卷积神经网络（Convolutional Neural Networks， CNN）与长短期记忆（Long Short-Term Memory，LSTM）网络相结合实现前视成像中方位向的预测，首先介绍了扫描前视成像信号的类卷积模型及其病态性，利用脉冲压缩以及距离徙动校正对回波信号预处理，输入CNN-LSTM神经网络逐距离单元进行方位向估计。仿真结果表明：算法能有效提高前视成像的方位分辨率，实现前视成像的超分辨。

Forward-looking Imaging Algorithm Based on CNN-LSTM Neural Network

As a difficulty and focus in the field of radar imaging, radar forward-looking imaging has broad application prospects in automatic driving, navigation, precision guidance and so on. The traditional forward-looking imaging algorithm is limited by the width of the antenna aperture and cannot achieve high-resolution imaging. In this paper, CNN ( Convolutional Neural Networks ) neural network and LSTM ( Long Short-Term Memory ) neural network are combined to realize the prediction of azimuth in forward-looking imaging. Firstly, the convolution-like model of the scanning forward-looking imaging signal and its ill-posedness are introduced. The echo signal is preprocessed by pulse compression and range migration correction, and input into the CNN-LSTM neural network to perform azimuth estimation by range unit. The simulation results show that the algorithm can effectively improve the azimuth resolution of forward-looking imaging and realize the super-resolution of forward-looking imaging.