The Super Resolution Imaging Technology Research Of SAR System Using Stepped Frequency Waveforms

Stepped-frequency Synthetic Aperture Radar (SAR) achieves high azimuth resolution by the synthetic aperture principle, while the high range resolution depends on the large bandwidth of the transmitted signal. The system range resolution is limited by the bandwidth of transmitted signal. As a result, the actual application requirement can not be satisfied for weak targets detection and discrimination.Vehicle-mounted Stepped-frequency Forward-looking Ground-penetrating Virtual Aperture Radar (SFGPVAR) system is designed to detect metal antitank mines.Effective methods for mine detection and discrimination require very high 2-dimensional imaging resolution, which is restricted in SFGPVAR system due to fixed aperture length and limited bandwidth. In order to improve the mines detection performance of SFGPVAR system effectively, 2-dimension super resolution imaging algorithm of combining Amplitude and Phase Estimation of a Sinusoid (APES) with Robust Capon Beamforming (RCB) is proposed which is called APES-RCB. Furthermore, Compressive Sensing (CS) is introduced according to the fact that mines and clutters are generally sparse in SAR images. In this paper, high resolution imaging and detection of mine targets under the condition of these two super resolution imaging algorithms are discussed.As for the algorithm APES-RCB, the basic principles and performances of APES and Capon are discussed at first. Having SFGPVAR system as a background, the flow of 2-dimension super resolution imaging method based on APES-RCB is described in detail. Its performance is analyzed based on experimental data process. And then, APES owns many advantages as a kind of spetral estimation methods. Consequently, an approach for suppression sidelobes of strong clutters in echo domain based on the APES is analyzed. Compared with traditional sidelobes suppression methods, strong clutters can be suppressed effectively by this approach and the disadvantage of suppressing weak targets can be overcome.As for the algorithm CS, the basic principle and application condition of CS are introduced at first. The reason of why mines and clutters are generally sparse in SAR images is analyzed as precondition. The character of the electromagnetic scattering of mines is also introduced. Afterwards, the whole flow of super resolution imaging method based on CS is described and it is validated by simulation and experimental results. Combined with the discrete scattering structure of metal mines and the invariance of azimuth scattering, the space scattering structure of mines can be extracted by CS. Then the structure is transformed into geometry characters which are related to physical structure of mines. By this way, a new approach of targets discrimination and identification based on CS theory is proposed. Though both the algorithms of APES-RCB and CS can achieve super resolution, they are quite different. First of all, there are many advantages of these two methods when they are applied to SFGPVAR system. Therefore, this chapter analyzes how to apply these two imaging methods to SFGPVAR system. In order to make a comparison of these two methods, mine targets section and the whole scene based on SFGPVAR system experimental data are imaged by these two methods respectively. The case of using incomplete data is done especially. The comparison results prove that the performance of algorithm CS is better. At last, the parameters which these two methods refer to when the signal-to-noise ratio is low are considered to be estimated. Their influence on imaging quality is also discussed simply. A new way of thinking about how to estimate the parameters is proposed combined with the actual application environment.