Abstract
Range alignment (RA) is the first step of inverse synthetic aperture radar (ISAR) translational compensation. However, the precision of traditional range alignment method for non-cooperative targets will dramatically decreases under sparse aperture condition. We propose a CRAN-RA (CNN RNN Attention mechanism Network-Range Alignment) method to address the problem by combining convolutional neural networks (CNN) and recurrent neural networks (RNN) with attention mechanism. The unified network can effectively integrate regional features extracted by CNN and temporal features extracted by RNN. Input unaligned echoes, the network can predict the aligned echoes. Compared with the traditional methods and RNN-based methods, the experiments show that the proposed network can significantly improve the alignment accuracy under sparse aperture and low SNR condition.
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