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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.