Abstract:

This research stresses the problem of the two-dimensional angle estimation in monostic cross multiple-input multiple-output (MIMO) radar. Parameters estimation accuracy could be improved with the Vandermonde-like structure in the source matrix, which is always ignored by existing estimation algorithms. With the centrosymmetric of the uniform linear array （ULA） and the Vandermonde-like structure of the data model, an improved trilinear decomposition algorithm is proposed for the two-dimensional angle estimation. The unitary transform is used to construct an expand data matrix, and the two-dimensional angle estimation is then linked to the trilinear model. Due to the expand output, the virtual aperture of the array is increased, hence the proposed trilinear algorithm performs better than the traditional trilinear algorithm. In addition, the proposed algorithm requires neither peak searching nor eigenvalue decomposition. Furthermore, the proposed algorithm could achieve automatic pairing of the two-dimensional angle. Simulation results verify the effectiveness of the proposed algorithm.