Computationally Efficient Direction of Arrival Estimation Without Subspace Decomposition

In this paper, a computationally efficient direction of arrival (DOA) estimation algorithm without subspace decomposition is presented with a uniform linear array (ULA). The ULA is divided into two sub-arrays and two noise-free cross-correlation matrices are computed using the data vectors received by the two sub-arrays. By rearranging the elements of a vector formed from the new constructed cross-correlation matrix, an equivalent noise subspace is obtained and the expensive subspace decomposition process is efficiently avoided. Numerical simulations are conducted to substantiate the effectiveness of the proposed method, and it is shown that the new technique can provide good performances with a low signal-to-noise ratio (SNR) and with a small number of snapshots.