Subspace Algorithm for 2-D DOA Estimation by Rearranging the Elements of Cross-correlation Matrix

This paper presents a subspace algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation with L-shaped array. In the proposed algorithm, we get the signal subspace by rearranging the elements of the cross-correlation matrix, then we generate a spectral function based on the signal subspace, and last we estimate the DOAs of sources by the 2-D spectral peak search. Since we utilize spectral peak search to estimate the DOAs of sources our method does not need the extra procedure to pair the 2-D angles of the sources. Comparing with the 2-D music algorithm, the estimation accuracy of the algorithm declines a bit, but our method does not need to perform eigenvalue decomposition, so the computational burden of our method is reduced and it is easy to be implemented. The derivation of the algorithm and detailed procedure are given in this paper. Simulations are presented to demonstrate the effectiveness of the proposed algorithm.