High-resolution direction of arrival estimation using minimum-norm method without eigendecomposition

The minimum-norm method (MNM) for high-resolution directions-of-arrival (DOA) estimation relies on special purpose hardware or software for obtaining the signal and noise subspace eigenvectors of autocorrelation (AC) matrices. It is shown in this paper that the DFT of the AC matrix (DFT-of-AC) essentially performs an equivalent task of separating the signal and noise subspaces. Furthermore, when the signal-subspace part of the DFT-of-AC vectors are used in the minimum-norm framework, almost identical high-resolution DOA estimates are produced. When compared with eigendecomposition-based MNM, the computational load of the proposed DFT-based approach (D-MNM) is lower but the bias, mean-squared error and the root locations are almost similar. The simulations further show that at low SNR the performance of D-MNM is more robust and it also has superior dynamic range