Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this prob...

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Bibliographic Details
Main Authors: Bokun Tian, Xiaoling Zhang, Liang Li, Ling Pu, Liming Pu, Jun Shi, Shunjun Wei
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
linear array synthetic aperture radar (LASAR)
compressed sensing (CS)
fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM)
three-dimensional (3D) imaging
high computational efficiency
Online Access:https://www.mdpi.com/2072-4292/13/9/1751

Internet

https://www.mdpi.com/2072-4292/13/9/1751

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