Sparse Bayesian Learning-Based Multichannel Radar Forward-Looking Superresolution Imaging Considering Grid Mismatch

Abstract

To overcome the effect of grid mismatch on the superresolution performance, a sparse Bayesian learning-based multichannel radar forward-looking superresolution imaging scheme is proposed in this article. In the scheme, a coarse imaging grid is initialized first, and local grid refinement is performed based on the preliminary estimation results of the target information. Then, based on the refined grid, an off-grid superresolution model considering grid mismatch is established, and the total least squares method is used to estimate the mismatch error for modifying the steering matrix in superresolution processing. At last, based on the modified steering matrix, sparse Bayesian learning algorithm is iteratively executed to achieve multichannel radar forward-looking superresolution imaging. Simulated and measured data processing results are illustrated to verify the effectiveness of the proposed scheme.

Keywords

• Forward-looking superresolution imaging
• grid mismatch
• multichannel radar
• sparse Bayesian learning