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Sparse Representation Based Autofocusing Technique for ISAR Images

机译：基于稀疏表示的ISAR图像自动聚焦技术

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摘要

From the perspective of sparse signal representation, an autofocusing method in inverse synthetic aperture radar imaging is proposed. Different from the idea of taking the entropy or contrast as the optimization objective in the presently existing algorithms, this method exploits the intrinsic sparsity distribution of scattering centers to compensate the indeterminacy of the measurement system, and a universal regularization model is constructed to simultaneously balance the measurement errors and the sparsity constraint. Accordingly, an effective iterative algorithm on the basis of solving a matrix equation and a trigonometric equation is proposed to estimate the phase errors, which makes the conventional minimum entropy method (MEM) a special case of the proposed method. Specifically, with the sparsity measure being selected as the logarithm function, an analytic representation is derived for the solution of the matrix equation, and the convergence and computational complexity of the proposed method is also discussed. Experimental results show that the proposed method outperforms the present data-driven algorithms in terms of efficiency and robustness, such as MEM, phase gradient autofocusing algorithm, and maximum contrast method.
机译：从稀疏信号表示的角度出发，提出了一种逆向合成孔径雷达成像的自动聚焦方法。与现有算法中以熵或对比度为优化目标的思想不同，该方法利用散射中心的内在稀疏性分布来补偿测量系统的不确定性，并构造了一个通用的正则化模型来同时平衡测量系统的不确定性。测量误差和稀疏性约束。因此，提出了一种基于求解矩阵方程和三角方程的有效迭代算法来估计相位误差，这使得传统的最小熵方法成为了该方法的一种特殊情况。具体地，选择稀疏度量作为对数函数，导出解析表示形式的矩阵方程式，并讨论了该方法的收敛性和计算复杂性。实验结果表明，该方法在效率和鲁棒性方面优于目前的数据驱动算法，例如MEM，相位梯度自动聚焦算法和最大对比度方法。

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