Deep learning network with new weighting strategy for ISAR image enhancement.

Cheng, Ping; Xu, Xinmiao; Yang, Xinkai; Nie, Yunqing; Chen, Weiyang; Qu, Zhiyu; Zhao, Jiaqun

In inverse synthetic aperture radar (ISAR) imaging, the conventional range-Doppler (RD) algorithm cannot obtain satisfactory imaging results for sparse aperture. Compressed sensing (CS) imaging methods, such as the typical iterative shrinkage and thresholding algorithm (ISTA), are often used in sparse aperture radar imaging. However, CS-based methods have high computational complexity and difficulty in setting parameters. To overcome the shortcomings, a new deep unfolding network named complex-valued weighted learning ISTA (CV-WLISTA) is proposed in this paper. The new network is very efficient and can learn the parameters adaptively. Based on ISTA and a new weighting strategy, it takes advantage of both the sparsity and the structural property of ISAR images to improve imaging performance. The experimental results show that CV-WLISTA has better reconstruction performance and higher efficiency compared with the traditional algorithms. Therefore, CV-WLISTA is an efficient ISAR imaging method with excellent performance.

DEEP learning; INVERSE synthetic aperture radar; THRESHOLDING algorithms; IMAGE intensifiers; COMPRESSED sensing

International Journal of Remote Sensing, 2024, Vol 45, Issue 9, p3003

0143-1161

Academic Journal

10.1080/01431161.2024.2339204