Wu Yong, Wang Jun. Application of Mixed Kalman Filter to Passive Radar Target Tracking[J]. Journal of Radars, 2014, 3(6): 652-659. doi: 10.12000/JR14113
Citation: Ren Xiaozhen, Yang Ruliang. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase[J]. Journal of Radars, 2016, 5(1): 65-71. doi: 10.12000/JR15135

Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase

DOI: 10.12000/JR15135
Funds:

The National Natural Science Foundation of China (61201390), The Key Scientific Research Project in Universities of Henan Province (16A510004), The Plan for Young Backbone Teacher of Henan Province (2015GGJS038)

  • Received Date: 2015-12-31
  • Rev Recd Date: 2016-01-24
  • Publish Date: 2016-02-28
  • Observation data obtained from the Four-Dimensional (4D) Synthetic Aperture Radar (SAR) system is sparse and non-uniform in the baseline-time plane. Hence, the imaging results acquired by traditional Fourier-based methods are limited by high side lobes. Compressive Sensing (CS) is a recently proposed technique that allows for the recovery of an unknown sparse signal with overwhelming probability from very limited samples. However, the standard CS framework has been developed for real-valued signals, and the imaging process for 4D synthetic aperture radar deals with complex-valued data. In this study, we propose a new 4D synthetic aperture radar imaging algorithm based on an iterative reconstruction of magnitude and phase, which transforms the height-velocity imaging problem of 4D synthetic aperture radar into a joint reconstruction problem of the magnitude and phase of the complex-valued scatter coefficient. Using the phase information in the algorithm, the image quality is improved. Simulation results confirm the effectiveness of the proposed method.

     

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