An Effective Algorithm for Direction-of-Arrival Estimation of Coherent Signals with ULA

In the field of array signal processing, multiple signal classification (MUSIC) algorithm is a classical spectrum estimation algorithm. However, when there are coherent signals, the rank of signal covariance matrix is generally less than the number of signals, which makes the estimation inaccurate. Taking uniform linear array (ULA) as an example, this paper presents a high-precision DOA estimation algorithm by reconstructing noise subspace. This algorithm uses not only the auto-covariance but also cross-covariance information and constructs a new augmented matrix with the auto-covariance matrix. Noise subspace and eigenvalue matrix can be obtained by singular value decomposition of matrix. For more reliable data, on the basis of a large number of experiments, a noise subspace consisting of the eigenvectors corresponding to the new eigenvalue matrix is reconstructed, and finally the DOA estimation is obtained through spectrum peak search. It is shown by the simulation results show that the improved algorithm can maintain the accuracy well of DOA with effect even under the conditions of low signal-to-noise ratio and small number of snapshots.