scholarly journals New SAR Imaging Algorithm via the Optimal Time-Frequency Transform Domain

2020 ◽  
Vol 65 (3) ◽  
pp. 2351-2363
Author(s):  
Zhenli Wang ◽  
Qun Wang ◽  
Jiayin Liu ◽  
Zheng Liang ◽  
Jingsong Xu
Keyword(s):  
Optimal Time ◽  
Transform Domain ◽  
Imaging Algorithm ◽  
Time Frequency ◽  
Sar Imaging

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

A RELAX Based Method for Spotlight SAR Imaging

2012 ◽  
Vol 500 ◽  
pp. 362-367
Author(s):  
Xiao Zhen Ren ◽  
Yao Qin ◽  
Hong Liang Fu
Keyword(s):  
Parameter Estimation ◽  
Estimation Problem ◽  
Noisy Environment ◽  
Parameter Estimation Problem ◽  
Imaging Algorithm ◽  
Imaging Results ◽  
Sar Imaging ◽  
Simulation Results ◽  
Noisy Condition

The imaging problem of spotlight SAR is converted to a parameter estimation problem of several monochromatic signals in additive white Gaussian noisy condition in this paper. Moreover, a spotlight SAR imaging algorithm based on RELAX is presented in detail. Traditional polar format algorithm and the presented method are applied to spotlight SAR imaging respectively, and the imaging results are compared. Simulation results show that the polar format algorithm doesn’t give satisfactory imaging results, while the proposed method adapts the noisy environment better and obtains better results.

One-Stationary Bistatic Side-Looking SAR Imaging Algorithm Based on Extended Keystone Transforms and Nonlinear Chirp Scaling

2013 ◽  
Vol 10 (2) ◽  
pp. 211-215 ◽  
Author(s):  
Zhongyu Li ◽  
Junjie Wu ◽  
Wenchao Li ◽  
Yulin Huang ◽  
Jianyu Yang
Keyword(s):  
Imaging Algorithm ◽  
Sar Imaging

scholarly journals Correcting Spatial Variance of RCM for GEO SAR Imaging Based on Time-Frequency Scaling

Sensors ◽  
2016 ◽  
Vol 16 (7) ◽  
pp. 1091 ◽  
Author(s):  
Ze Yu ◽  
Peng Lin ◽  
Peng Xiao ◽  
Lihong Kang ◽  
Chunsheng Li
Keyword(s):  
Frequency Scaling ◽  
Spatial Variance ◽  
Time Frequency ◽  
Sar Imaging
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