Laser Radar(LADAR) is a sensitive Radar which can obtain high measurement resolution that might be hardly achieved by the microwave Radar. More Doppler frequency shift and more geometric structure information can be obtained by using LADAR in detection of target with micro-motion. According to the micro-motion model of target, an algorithm based on the graphical electromagnetic computing(GRECO) is proposed for the dynamic LRCS simulation of complex target. The LRCS of each small triangular plane element is calculated firstly and then summed for a total LRCS of the whole target. The examples of simulation result in various micro motion forms are given and the results match well with the theoretical results.
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.