Two-dimensional DOA estimation algorithm based on empty holes data filling by using tensor modeling

Tensor modeling can effectively improve the two-dimensional DOA estimation performance of the coprime planar array. However, the virtual array it constructs is not completely continuous, which will cause some virtual array elements to be unused, and the loss of the virtual array aperture. In this paper, a two-dimensional DOA estimation algorithm based on empty holes data filling is proposed by using tensor modeling. The algorithm first constructs a virtual uniform planar array with a large aperture, and uses the covariance tensor of the received signal to fill in the data of the corresponding position, and the data is set to 0 for the unfilled position. Then the SVT algorithm is used to estimate the unfilled position data to obtain the covariance tensor of the virtual uniform planar array. Finally, the DOA of the signal is obtained by combining CP decomposition and spectral peak search. Simulation experiments show that the proposed algorithm effectively improves the estimation accuracy of the algorithm.