A Computationally Efficient Airborne Forward-Looking Super-Resolution Imaging Method Based on Sparse Bayesian Learning

Abstract

In airborne forward-looking imaging, the azimuth resolution and the imaging efficiency are important. In this paper, we propose a low-dimension sparse Bayesian learning with Doppler compensation (LDSBL-DC) method to improve the azimuth resolution with a low computational complexity in airborne forward-looking imaging. First, since the variant pitching angle causes the space-variant of the Doppler centroid, the Doppler convolution matrix needs to be constructed in each range cell. We construct a Doppler compensation matrix to eliminate the space-variant of the Doppler centroid. After the Doppler centroid compensation, the Doppler convolution matrix only needs to be constructed once. Second, we propose a low-dimensional projection model based on the singular value decomposition. In the low-dimensional projection model, the high-dimension echo data is compressed to low-dimension data. Finally, combining Doppler centroid compensation and low-dimensional projection model, a new forward-looking imaging model is created, and we introduce sparse Bayesian learning (SBL) to estimate the imaging parameters. In the estimation of the targets’ scattering coefficient, we reduce the computational complexity by the matrix transformation. Several simulations are designed to evaluate the performance of the efficient forward-looking imaging method. The simulation results show the LDSBL-DC method can improve the azimuth resolution with a low computational complexity.