Filter Back-Projection(FBP) algorithm is usually used to reproduce the target image based on polar coordinate format data. The traditional method achieves higher imaging resolution by increasing bandwidth and enlarging the target rotation angle. In practical applications, limited echo data can be obtained due to the reasons from the equipment and the detection targets. Spectral estimation algorithms such as Apes has been widely used in Radar imaging, which can obtain complex spectral estimation with more narrow spectral peaks and lower side-lobes compared with FFT methods. Thus, this paper proposes a technique to achieve higher resolution which using spectral estimation instead of the filtering process in FBP. Simulation results show the efficiency and the accuracy of the presented approach.
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.