In this paper, a low sparse Bayesian learning mixed source DOA estimation algorithm is proposed based on the principle of millimeter-wave radar velocity measurement of speed and distance, aiming at the problem of disturbance affecting the positioning accuracy of object location, that is, Sparse Bayesian Learning for Low-rank and Sparse recovery, SBL-LSR. Due to the low-rank property of fixed sources and the sparsity of mobile sources under multiple fast beats, The SBL-LSR algorithm converts the DOA estimation of all fast shot mixed signals into low-rank matrix and sparse matrix recovered from the observed matrix. The SBL-LSR algorithm uses the sparse Bayesian learning framework to provide prior settings of parameters to be estimated, so that the SBL-LSR algorithm shows excellent performance in the estimation of mixed sources, and can maintain high accuracy even under noisy disturbances. Finally, combining with the beam forming technology, the vehicle object location experiment is carried out to verify the effectiveness of the proposed algorithm.