Novel Methods to Accelerate CS Radar Imaging by NUFFT

• Published in: IEEE transactions on geoscience and remote sensing Vol. 53; no. 1; pp. 557 - 566
• Main Authors: Sun, Shilong, Zhu, Guofu, Jin, Tian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Accuracy; Algorithm design and analysis; Compressive sensing (CS); Computational complexity; Computer simulation; Data imaging; Efficiency; fast Gaussian gridding; Fourier transforms; Gaussian; Imaging; Memory management; Methods; nonuniform fast Fourier transform (NUFFT); Projection; Radar imaging; Real time; Spectra; spectral projection gradient ell_{1}-norm (SPGL1); fast Gaussian gridding; spectral projection gradient ell_{1}-norm (SPGL1); nonuniform fast Fourier transform (NUFFT); Compressive sensing (CS)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2014.2325492

Soon after its innovation, compressive sensing (CS) was rapidly applied to radar imaging. However, the huge computational complexity and the memory requirements have become the bottlenecks in its widespread applications to large-scale and real-time radar imaging. In this paper, two novel methods based on fast Gaussian gridding nonuniform fast Fourier transform are proposed to speed up CS radar imaging and reduce the memory requirement. By using the proposed methods, the application of CS imaging method can be extended to large-scale and real-time radar imaging with high reconstructing efficiency and small memory requirement. Theoretical analysis and numerical results from the aspects of accuracy, efficiency, and memory requirement validate the proposed methods. Simulation and real data imaging results by spectral projection gradient ℓ 1 -norm method are given to further demonstrate the efficiency of the proposed methods.