This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output(MIMO) radar with arbitrary arrays in spatial colored noise. A novel method for joint estimation of Doppler frequency, two-dimensional(2D) direction of departure and 2D direction of arrival based on the propagator method(PM) for arbitrary arrays is discussed. A special matrix is constructed to eliminate the influence of spatial colored noise. The four-dimensional(4D) angle and Doppler frequency are extracted from the matrix and the threedimensional(3D) coordinates of the targets are then calculated on the basis of these angles. The proposed algorithm provides a lower computational complexity and has a parameter estimation very close to that of the ESPRIT algorithm and the DOA-matrix algorithm in the high signal to noise ratio and the Cram ′er-Rao bound(CRB) is given. Furthermore, multi-dimensional parameters can be automatically paired by this algorithm to avoid performance degradation resulting from wrong pairing. Simulation results demonstrate the effectiveness of the proposed method. This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays in spatial colored noise. A novel method for joint estimation of Doppler frequency, two-dimensional (2D) direction of departure and 2D direction of arrival based on the propagator method (PM) for arbitrary arrays is discussed. A special matrix is ​​constructed to eliminate the influence of spatial colored noise. The four-dimensional (4D) angle and Doppler frequency are extracted from the matrix and the threedimensional (3D) coordinates of the targets are then calculated on the basis of these angles. The proposed algorithm provides a lower computational complexity and has a parameter estimation very close to that of the ESPRIT algorithm and the DOA-matrix algorithm in the high signal to noise ratio and the Cram ’er-Rao bound (CRB) is given. Furthermore, multi-dimensional parameters can be automatically paired by this algorit hm to avoid performance degradation resulting from wrong pairing. Simulation results demonstrate the effectiveness of the proposed method.