Abstract
3-D imaging based Radar cross section (RCS) measurement technology is a flexible and efficient RCS measurement technology that has emerged in recent years. This technology achieves the separation of the target and the environment through the near-field 3-D high-resolution imaging. And then the far-field RCS of the target can be obtained through the near-field-to-far-field transformation, therefore the technology has near-field and outer-field measurement capabilities. For imaging-based RCS measurement, its imaging accuracy is correlated with RCS measurement accuracy. The imaging resolution of the measurement system limits the accuracy of the target imaging and the reconstruction of the target shape, which in turn affects the RCS accuracy. Therefore, imaging resolution is one of the limiting factors that affect the accuracy of imaging-based RCS measurement. The current super-resolution imaging method can exceed the resolution of traditional imaging and improve the accuracy of imaging-based RCS measurement. In this paper, A RCS measurement method based on compressed sensing 3-D super-resolution is discussed. This method applies the 3-D super-resolution imaging of compressed sensing to the RCS measurement based on linear array (SAR) 3-D imaging. According to the imaging model, an appropriate observation matrix is designed, and the orthogonal matching pursuit (OMP) algorithm is used to reconstruct the super-resolution 3-D image of the target. The image can improve the accuracy of reconstruction of the target’s shape, and then improve the accuracy of RCS obtained. Compared with the traditional BP imaging algorithm and compressed sensing imaging without super-resolution, the RCS measurement accuracy is significantly improved, which verified by FEKO simulation.
Published Version
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