This paper addresses the direction of arrival(DOA) estimation problem for the co-located multiple-input multipleoutput(MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing(CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio(SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification(MUSIC) algorithm and other CS recovery algorithms. This paper addresses the direction of arrival (DOA) estimation problem for the co-located multiple-input multiple output (MIMO) radar with random arrays. The spatially distributed sparsity of the targets makes compressive sensing (CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio (SNR) scene. the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional m ultiple signal classification (MUSIC) algorithm and other CS recovery algorithms.