Digital Signal Processing and Atomic Functions in Synthetic Aperture Radar

2007 ◽  
Vol 66 (16) ◽  
pp. 1441-1452 ◽  
Author(s):  
Victor Filippovich Kravchenko ◽  
V. K. Volosyuk ◽  
V. V. Pavlikov
2012 ◽  
Vol 433-440 ◽  
pp. 2004-2010
Author(s):  
Su Yi Mon Su ◽  
Jian Cheng Fang

Digital signal processing is used to focus the image and obtain a higher resolution than achieved by conventional radar systems. This paper presents details of Synthetic Aperture Radar (SAR) signal processing and imaging technique with the goal of generating images. A Matlab based program is developed and coded for imaging simulation. It facilitates processing data and producing desired output. Then, we investigate the characteristics of Linear Frequency Modulated (LFM) signal prior to getting image results. The SAR properties in range and azimuth directions are described. The received signal and SAR raw data is theoretically described. In addition, the target reflection signal processing is also well presented. The SAR image formation is described using Range Doppler Algorithm (RDA). Finally, simulation parameters are computed and imaging simulation test is finished.


2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Le-tian Zeng ◽  
Chun-hui Yang ◽  
Mao-sheng Huang ◽  
Yue-long Zhao

In the signal processing software testing for synthetic aperture radar (SAR), the verification for algorithms is professional and has a very high proportion. However, existing methods can only perform a degree of validation for algorithms, exerting an adverse effect on the effectiveness of the software testing. This paper proposes a procedure-based approach for algorithm validation. Firstly, it describes the processing procedures of polar format algorithm (PFA) under the motion-error circumstance, based on which it analyzes the possible questions that may exist in the actual situation. By data simulation, the SAR echoes are generated flexibly and efficiently. Then, algorithm simulation is utilized to focus on the demonstrations for the approximations adopted in the algorithm. Combined with real data processing, the bugs concealed are excavated further, implementing a comprehensive validation for PFA. Simulated experiments and real data processing validate the correctness and effectiveness of the proposed algorithm.


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