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.
Forward-looking imagingDeep learningConvolutional neural networkIll-posed inverse problem
万方数据
维普
