RBM-based joint dictionary learning for ISAR resolution enhancement

Autor: Jiaqi Ye, Dan Qin, Yifan Zhang, Xunzhang Gao
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: The Journal of Engineering (2019)
Druh dokumentu: article
ISSN: 2051-3305
DOI: 10.1049/joe.2019.0732
Popis: In this study, an inverse synthetic aperture radar (ISAR) image resolution enhancement algorithm based on joint dictionary learning is proposed, by which two special sets of sparse signals called dictionaries are solved by exploiting numerous high-resolution (HR) and low-resolution (LR) ISAR images. Herein a new coupled dictionary learning algorithm based on restricted Boltzmann machine (RBM) is designed to learn a LR and a HR dictionary using LR and HR image patches. Since the echoes are equivalent to similar scattering-centre models when an object is illuminated by radar signals with same centre frequency and different bandwidth, respectively, it is reasonable to assume the object's LR ISAR image shares the same sparse representation coefficients with its HR ISAR image. When a LR ISAR image is represented sparsely with a LR dictionary, a HR ISAR images can be reconstructed based on a HR dictionary owing to the similar sparse representation coefficients. Experiment results with simulation data demonstrate the superior performance of the proposed method over other classical dictionary training algorithms.
Databáze: Directory of Open Access Journals
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