Study On Electromagnetic Simulation Of Radar Imaging And Super-Resolution Algorithms

Radar imaging is one of extremely important and widely used technologies in military and national defense modernization. For a long time, much more attentions have been focused on how to obtain higher resolution, but fewer researches have been done on physical understanding the nature of radar imaging. In fact this understanding could help us identify target better. In radar imaging, they are obvious difference between radar images generated by processing radar echoes directly collected in the near-field of the radar antenna and radar images generated by synethetically processing the collected radar echoes in far-field of antenna, although the imaged targets are indeed in the near-field of the equivalent synthesized large antenna. However fewer studies have been conducted on investigating the similarities and dissimilarities from the point of view of imaging mechanism.In this dissertation, we firstly developed a full-wave electromagnetic field radar imaging simulator based on FDTD (Finite Difference Time Domain) method, and by using the model, we simulated near-field radar echoes, and then performed imaging processing on the echo data by using TDC (Time Domain Correlation), BP (Back Projection) and PSM (Phase Shift Migration) imaging algorithms. Through simulation we found that although the PSM algorithm is equivalent to the weighted BP algorithm in the far-field case, but they are not equivalent in the near-field case. We explained the difference based on the relation between the signal spectrum and the projected spatial spectrum, and we further explained the physical meaning of"Zero-padding"operation in the PSM algorithm from the perspective of spatial spectrum projection.For investigating the difference between direct near-field imaging and the"equivalent near-field imaging"obtained by synthesizing the far-field data in azimuthal direction, by establishing the radiation model of electric dipole, we compared the radar imaging in these two cases from electromagnetic point of view through analyzing radar waveform, the induced electric currents and magnetic currents and the re-radiation of the induced currents focusing on the amplitude, phase and frequency aspects. Based on physical mechanism understanding we summarized the characteristics of near-field imaging and equivalent near-field imaging.Super-resolution imaging is a kind of technique used in radar for obtaining much higher resolution even with a small range of observation angle and small signal bandwidth, but usually it is achieved with large observation angle range and large bandwidth. In this dissertation we studied two super-resolution algorithms, i.e. MUSIC (Multiple Slgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithms and statistically analyzed their performance. In MUSIC study, we show that two-dimensional MUSIC (2D-MUSIC) algorithm by using spatial smoothing technique is equivalent to one-dimensional algorithm. Based on statistical viewing point and applying eigenvalues and eigenvectors of the auto-correlation matrix, we derived theoretically the equation for describing the performance estimation of 2D-MUSIC algorithm. By using the statistic characteristics of spatial spectrum, we derived the uniqueness conditions for 2D-MUSIC algorithm, and analyzed the reasons resulting in malfunction and the different parameters influencing the performance of 2D-MUSIC. We analyzed the performance of 1D-ESPRIT applied in azimuthal direction for ISAR and some parameters affecting it. At the same time, we applied RD, 2D-MUSIC, and 2D-ESPRIT algorithms to processing the simulated electromagnetic far-field signals generated by FDTD simulator, and compared these three algorithms from aspects of resolution, calculation burden, and algorithm stability, explained why 2D-ESPRIT can get higher resolution than 2D-MUSIC does.