Abstract: Based on the sparsity of inverse synthetic aperture radar (ISAR) signal, an ISAR high-resolution imaging algorithm based on mixed l2,0 norm sparse constraint is proposed in this paper. In this method, an optimal ISAR signal model based on mixed norm sparse constraint is established by using compressed sensing theory, and the high-resolution reconstruction of ISAR image in short coherent accumulation time is realized by solving the optimal model. The model makes use of the advantages of l2,0 mixed norm, which can achieve faster convergence and improve the operation speed. At the same time, the conjugate gradient descent method and fast Fourier transform are used to solve the optimization model, which improves the operation efficiency of the algorithm. Simulation data and measured data verify the effectiveness of the proposed method.