Deep convolutional neural network for P-band spaceborne synthetic aperture radar imaging through the ionosphere

Abstract

The dispersion characteristics of the background ionosphere and the random fluctuations of the ionospheric irregularities are an important source of phase error that seriously damages the quality of radar images. To mitigate the ionospheric distortions of P-band spaceborne synthetic aperture radar (SAR) images, a super-resolution deep learning method is proposed in this paper. Different from the traditional imaging method based on the prior knowledge of imaging, the method proposed in this paper directly trains the ionospheric implicit imaging model of spaceborne SAR without complicated iterative processes. First, the phase errors, which are caused by the dispersion and the scintillation in the range and azimuth directions, respectively, are analyzed. Second, an improved u-net structure that embeds the residual network between the encoder and decoder is briefly proposed. Finally, the range doppler algorithm is used to preprocess radar image as the input of the convolutional neural network (CNN) and is compare with the predicted output of the CNN. Experimental results prove the effectiveness of the proposed method in radar image focusing.