The amplitude and phase estimation (APES) algorithm is widely used in modern spectral analysis. Compared with conventional Fourier transform (FFT), APES results in lower sidelobes and narrower spectral peaks. However, in synthetic aperture radar (SAR) imaging with large scene, without parallel computation, it is difficult to apply APES directly to super-resolution radar image processing due to its great amount of calculation. In this paper, a procedure is proposed to achieve target extraction and parallel computing of APES for super-resolution SAR imaging. Numerical experimental are carried out on Tesla K40C with 745 MHz GPU clock rate and 2880 CUDA cores. Results of SAR image with GPU parallel computing show that the parallel APES is remarkably more efficient than that of CPU-based with the same super-resolution.

Date Published: 20 October 2015
PDF: 9 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460C (20 October 2015);

Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang D.D.S.; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)