Adaptive Filtering for 3D SAR Data based on Dynamic Gaussian Threshold

3D synthetic aperture radar (SAR) is one of the hotspots in microwave imaging researches, which can implement the high 3D resolution and visually present 3D radar images of measured objects. Because the data amount and complexity of 3D SAR is much higher than traditional 2D SAR, it is required to study how to acquire the valid information and filter the invalid information (such as clutter and noise) precisely and quickly. This paper presents an adaptive filtering algorithm for 3D SAR data based on the dynamic Gaussian threshold (DGT). According to a given focal point for 3D SAR imaging, a group of dynamic Gaussian functions is introduced to filter the invalid information adaptively. Since there is the parallel relationship between the proposed DGT filtering algorithm and 3D SAR imaging algorithms, the proposed algorithm can satisfy the requirement for precision and efficiency simultaneously, without the post-processing of 3D SAR results. The actual experiment results show that the proposed algorithm can improve the signal-to-clutter and noise ratio (SCNR) about 40 dB for the point target and about 30 dB for the surface target.