Abstract: ‍ ‍The antenna aperture of scanning radar systems is limited by the platform size. As a result， scanning radar systems suffer from the disadvantage of low angular resolution. Angular super-resolution technology can obtain an angular resolution beyond the beam width determined by real aperture using signal processing method without changing the radar hardware system. Recent studies have shown that sparse angular super-resolution method based on minimum absolute shrinkage and selection operator （LASSO） can obtain higher resolution than traditional super-resolution methods. However， this method is based on the white Gaussian noise hypothesis and imposes the $l_{\mathrm{2}}$-norm on the residual items. When the heavy-tailed noise exists in the echo data， the $l_{\mathrm{2}}$-norm constraint in the LASSO method cannot effectively suppress the heavy-tailed noise， resulting in degraded angular resolution and false targets. To solve this problem， this paper presents an angular super-resolution imaging method to suppress the heavy-tailed noise. First， a minimum absolute deviation （LAD）-LASSO constraint criterion is introduced to suppress the heavy-tailed distribution noise. Furthermore， the optimal regularization parameter is selected based on the covariance fitting criterion to solve the problem of adaptive selection of regularization parameters in the model. Finally， in order to solve the non-smooth cost function， an iterative weighted least squares （IRLS） algorithm is presented. The $l_{\mathrm{1}}$-norm is replaced by the iterative weighted $l_{\mathrm{2}}$-norm， and the weights are calculated in each iteration to achieve the optimal solution. The results of simulation show that the proposed method can effectively suppress heavy-tailed noise and improve the scanning radar angular resolution under varying signal-to-the-noise ratio. Meanwhile， real data set acquired by a shore-based X-band radar demonstrate the effectiveness of the proposed method.