Inverse Synthetic Aperture Radar Imaging Using an Attention Generative Adversarial Network

Abstract

The traditional inverse synthetic aperture radar (ISAR) imaging uses matched filtering and pulse accumulation methods. When improving the resolution and real-time performance, there are some problems, such as the high sampling rate and large amount of data. Although the compressed sensing (CS) method can realize high-resolution imaging with small sampling data, the sparse reconstruction algorithm has high computational complexity and is time-consuming. The imaging result is limited by the model and sparsity hypothesis. We propose a novel CS-ISAR imaging method using an attention generative adversarial network (AGAN). The generator of AGAN is a modified U-net consisting of both spatial and channel-wise attention. The trained generator can learn the imaging operation from down-sampling data to high-resolution ISAR images. Simulations and measured data experiments are given to validate the advantage of the proposed method.

Keywords

• inverse synthetic aperture radar imaging
• attention generative adversarial network
• compressed sensing