The application discloses a two-dimensional
sparse array single-shot DOA
estimation method based on a deep unfolding convolutional network, and belongs to the technical field of cross array
signal processing and
deep learning. First, a two-dimensional uniform
planar array is constructed, and sparse
observation data are constructed. Then, an isomorphic
tensor mapping mode of separating real parts and imaginary parts is adopted to decompose the sparse observation signals and splice them along the channel dimension, so that a three-dimensional input
tensor containing real part channels and imaginary part channels is constructed to maintain the two-dimensional spatial topological structure of the array. Then, the overall structure of the deep unfolding network is unfolded, and a sliding window rule with a predetermined size is used to rearrange the two-dimensional array data into a block
Hankel matrix. Finally, network training is performed to realize DOA
estimation. The application maintains the two-dimensional
spatial structure, improves the reconstruction accuracy, reduces the computational complexity, improves the robustness under a low
signal-to-
noise ratio condition, realizes high-precision DOA
estimation under a single-shot condition, and has good
engineering application value.