ISAR High Resolution Imaging Algorithm Based on Weighted Adaptive Mixed Norm

Abstract

Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, this paper proposes a high resolution imaging algorithm for ISAR based on weighted adaptive mixed norm. By weighting against l_2,0 mixed norm term, an improved model of the sparse constraint ISAR signal is proposed. The model effectively distinguishes the signal and noise by adding the weight coefficient, and improves the reconstruction accuracy of the strong scattering center. Meanwhile, the weight coefficients in this improved model can be iteratively updated in each cycle to improve the image reconstruction accuracy. The optimization model takes advantage of mixed norm to achieve fast convergence in the operation, and adopts conjugate gradient descent method and fast Fourier transform operation in the solution, which simplifies the solving process of the optimization problem and improves the operation efficiency of the algorithm. Simulation data and measured data verify the effectiveness of the proposed method.