Removement of Staggered SAR Ambiguity in Low-Oversampling by Deep Learning

Staggered SAR can simultaneously get the ability of high azimuth resolution and wide range swath. However, it also causes pairs of azimuth ambiguities on imaging result because of its non-uniform sampling mode. In this paper, we proposed an innovative ambiguity removal method based on deep learning algorithm with a deep fully convolution and residual neural network. Traditional techniques are constrained in the steps of signal recovering and resampling thus only work at high over-sampling rate. Different from them, the deep learning method doesn't need to record the lost pulses for signal reconstruction and performances well at low over-sampling rate. The simulation results verify the effectiveness of our method and it behaves better than other traditional method.