Recently, the iterative adaptive approach (IAA) has been widely applied to enhance the azimuth resolution of real beam mapping (RBM) imagery. However, the IAA suffers from extremely high computational complexity in practice. This paper proposes a Split IAA for sparse scene to reduce the complexity. First, the IAA cost function is decomposed. Then the echo data is split into blocks, and the iterative model is redefined according to the target block and its corresponding cost function. Consequently, the high-dimensional data inversion problem is decomposed into multiple low-dimensional sub-problems to achieve fast super-resolution imaging. The measured results show that the proposed Split IAA significantly reduces the computational complexity without affecting super-resolution performance.