基于CNN-LSTM神经网络的前视成像算法

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

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。

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 CNNLSTM 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.