Extended Target Reconstruction of Airborne Real Aperture Array Radar by Adaptive Hybrid Regularization

Abstract

Hybrid regularization methods can be applied in airborne real aperture array radar (RAAR) to improve its angular resolution by combining the advantages of different regularization norms. However, the scale information of the extended targets cannot be accurately obtained because its reconstructed performance is related to the selected regularization parameters. In this paper, to accurately observe the scale information of extended targets, an adaptive hybrid regularization (AHR) method is proposed by a data-adaptive reweighted strategy. First, the generalized sparse (GS) regularization norm and the generalized total variation (GTV) regularization norm are combined to enhance the angular resolution and scale information of extended targets simultaneously. Second, a data-adaptive reweighted strategy is proposed to reduce the number of selected regularization parameters. Finally, simulations are carried out to verify the reconstructed performance of the proposed method. Based on the proposed AHR method, the scale information of the extended targets can be accurately obtained by adaptively selecting proper regularization parameters.