Azimuth Multichannel SAR Imaging Algorithm Based on Spectral Extrapolation and Image Fusion

The azimuth multichannel synthetic aperture radar (SAR) technique can simultaneously achieve high resolution and wide swath, which is important for disaster management, sea and land traffic observation, and environmental monitoring. However, nonuniform sampling leads to multichannel imaging quality degradation such as serious ghost targets or deterioration of signal-to-noise ratio (SNR). In this article, a multichannel SAR imaging algorithm is proposed. The key is to perform spectral selection and extrapolation based on the time-frequency characteristics of multichannel signals under nonuniform sampling conditions to obtain subimages with different ghost distributions. By implementing subimage fusion, the final imaging result can be obtained. Simulations and satellite real data experiments are conducted to verify the effectiveness of the proposed algorithm. The imaging performances of the proposed algorithm in terms of ghost-to-real target ratio (GRTR), and SNR are investigated with respect to the pulse repetition frequency (PRF). Moreover, simulation results also demonstrate that the proposed algorithm exhibits a more robust and consistent overall performance than existing methods.