A New BiRNN-SVA Method for Side Lobe Suppression

Abstract

The spatially variant apodization (SVA) algorithm, a classic super-resolution method for synthetic aperture radar (SAR) images, can suppress side lobes while maintain the resolution of the main lobe. To address the problem of residual side lobes or loss of main lobe energy in improved SVA algorithms, the article proposes a new side lobe suppression method combining the bidirectional recurrent neural network (BiRNN) and the SVA algorithm, employing BiRNN to extract the main lobe and side lobe features of radar data to achieve side lobe suppression at any Nyquist sampling rate. The land flight experiment data of the fully polarized microwave scatterometer is used to quantitatively evaluate the side lobe suppression performance and the main lobe energy in order to verify the effectiveness of the BiRNN-SVA method. The experimental results demonstrate that the BiRNN-SVA method can be applied to data at any Nyquist sampling rate and has superior PSLR and ISLR compared to the GSVA algorithm and MSVA algorithm. The image processed with the proposed method retains more fine details and edge features. In comparison to the GSVA algorithm and MSVA algorithm, the image contrast and focus have increased by 31.6% and 3.6%, respectively, and by 4.4% and 1.1%.

Keywords

• BiRNN
• side lobe
• super resolution imaging
• spatially variant apodization (SVA) algorithm