You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
7 October 2014Sparse radar imaging using 2D compressed sensing
Radar imaging is an ill-posed linear inverse problem and compressed sensing (CS) has been proved to have
tremendous potential in this field. This paper surveys the theory of radar imaging and a conclusion is drawn
that the processing of ISAR imaging can be denoted mathematically as a problem of 2D sparse decomposition.
Based on CS, we propose a novel measuring strategy for ISAR imaging radar and utilize random sub-sampling
in both range and azimuth dimensions, which will reduce the amount of sampling data tremendously. In order
to handle 2D reconstructing problem, the ordinary solution is converting the 2D problem into 1D by Kronecker
product, which will increase the size of dictionary and computational cost sharply. In this paper, we introduce the
2D-SL0 algorithm into the reconstruction of imaging. It is proved that 2D-SL0 can achieve equivalent result as
other 1D reconstructing methods, but the computational complexity and memory usage is reduced significantly.
Moreover, we will state the results of simulating experiments and prove the effectiveness and feasibility of our
method.
The alert did not successfully save. Please try again later.
Qingkai Hou, Yang Liu, Zengping Chen, Shaoying Su, "Sparse radar imaging using 2D compressed sensing," Proc. SPIE 9252, Millimetre Wave and Terahertz Sensors and Technology VII, 92520T (7 October 2014); https://doi.org/10.1117/12.2067223