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| Citation: | WEI Wei, ZHU Daiyin, and WU Di. Wavenumber domain algorithm based on the principle of chirp scaling for SAR imaging[J]. Journal of Radars, 2020, 9(2): 354–362. doi: 10.12000/JR19112
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Wavenumber Domain Algorithm Based on the Principle of Chirp Scaling for SAR Imaging
DOI: 10.12000/JR19112
More Information-
Abstract
As a frequency-domain algorithm for Synthetic Aperture Radar (SAR) imaging, the Range Migration Algorithm (RMA) can theoretically achieve optimal performance. However, because its Stolt mapping is performed using pixel-by-pixel convolution, the computational efficiency of RMA is inadequate for massive SAR data processing requirements. In this paper, we propose a modified RMA based on the Principle of Chirp Scaling (PCS). First, SAR echo data is divided along the range direction, and the subswath signal is compensated by the second-order range-azimuth coupling term and high-order terms at the reference distance. Then, the nonlinear Stolt mapping is modified to become linear. Finally, Stolt interpolation is realized using PCS to efficiently resample the processed data. Demonstrating both well-focused performance and high computational efficiency, the proposed PCS-RMA employs only fast Fourier transforms and complex vector multiplication operations to achieve modified Stolt mapping. The processing results of several simulation data and X-band-measured airborne SAR data with a pulse bandwidth of 1.2 GHz verify the effectiveness of the proposed algorithm. The proposed algorithm can also be employed for the rapid processing of missile-borne, spaceborne, and drone-borne SAR data. -
References
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1. 卢浩琴，朱莉，高传斌，刘浩. 基于不同窗函数的NUFFT全息成像算法. 微波学报. 2021(S1): 199-202 . 
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WEI Wei, ZHU Daiyin, and WU Di. Wavenumber domain algorithm based on the principle of chirp scaling for SAR imaging[J]. Journal of Radars, 2020, 9(2): 354–362. doi: 10.12000/JR19112
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- Figure 1. Geometric model of spotlight mode
- Figure 2. Residual phase error
- Figure 3. Processing flow of modified Stolt interpolation
- Figure 4. Processing flow of PCS-RMA
- Figure 5. Geometric relationship of point target distribution
- Figure 6. Contours of point target IRF
- Figure 7. Range profiles of point target IRF
- Figure 8. Azimuth profiles of point target IRF
- Figure 9. Comparisons of data processing time
- Figure 10. Processing results of the traditional RMA
- Figure 11. Processing results of the proposed PCS-RMA