Range Alignment in ISAR Imaging Based on Deep Recurrent Neural Network

Abstract

Envelope alignment is one of the key steps for inverse synthetic aperture radar (ISAR) translational compensation. The traditional envelope alignment method cannot be accurately completed under a low signal-to-noise ratio (SNR), which will limit the accuracy of subsequent phase focusing. We propose a deep recurrent neural network (RNN) frame to address the problem. This is an end-to-end learning approach. Radar echo pulses are input to the network one by one according to time sequence. The inputs of each layer can be divided into two parts. The one is the current pulse, and the other one, named “state,” is the outputs of the previous layer except for the aligned pulse. Moreover, the outputs of each layer contain the “state” for the next layer and the aligned result of the input pulse. The above structure is a typical RNN, and the “states” transform the time-sequence information between different pulses. Compared with the traditional methods, the experiments verify that the proposed network can not only provide better alignment accuracy under low SNR but also require a shorter alignment time.