Compressive sensing‐based two‐dimensional scattering‐center extraction for incomplete RCS data
We propose a two‐dimensional (2D) scattering‐center‐extraction (SCE) method using sparse recovery based on the compressive‐sensing theory, even with data missing from the received radar cross‐section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization t...
Main Authors: | Ji‐Hoon Bae, Kyung‐Tae Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
Electronics and Telecommunications Research Institute (ETRI)
2020-05-01
|
Series: | ETRI Journal |
Subjects: | |
Online Access: | https://doi.org/10.4218/etrij.2019-0017 |
Similar Items
-
Synthetic Aperture Radar Image Formation Via Sparse Decomposition
Published: (2011) -
On an Improved Iterative Reweighted Least Squares Algorithm in Robust Estimation
by: FANG Xing, et al.
Published: (2018-10-01) -
Incremental Localization Algorithm Based on Regularized Iteratively Reweighted Least Square
by: Yan Xiaoyong, et al.
Published: (2016-09-01) -
Compressed Sensing : Algorithms and Applications
by: Sundman, Dennis
Published: (2012) -
Electrical Faults Signals Restoring Based on Compressed Sensing Techniques
by: Milton Ruiz, et al.
Published: (2020-04-01)