Efficiency of subspace-based DOA estimators
Signal Processing
IEEE Transactions on Signal Processing
Maximum likelihood estimation for array processing in colored noise
IEEE Transactions on Signal Processing
Direction finding using noise covariance modeling
IEEE Transactions on Signal Processing
Stochastic Maximum-Likelihood DOA Estimation in the Presence of Unknown Nonuniform Noise
IEEE Transactions on Signal Processing - Part I
DOA Estimation and Detection in Colored Noise Using Additional Noise-Only Data
IEEE Transactions on Signal Processing
Maximum likelihood DOA estimation and asymptotic Cramer-Rao boundsfor additive unknown colored noise
IEEE Transactions on Signal Processing
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In subspace-based method for direction-of-arrival (DOA) estimation of signal wavefronts, the additive noise term is often assumed to be spatially white or known to within a multiplicative scalar.When the noise is nonwhite but has a known covariance matrix, we can still handle the problem through prewhitening. However, the problem turns to be complex when the noise field is completely unknown. In this paper, we study the localization of the sources, when the noise covariance matrix is one unknown band matrix. An iterative denoising algorithm based on the noise subspace spanned by the eigenvectors associated with the smallest eigenvalues is developed. The performance of the proposed algorithm is evaluated by computer simulations.We also test the proposed algorithm with some experimental data recorded during an underwater acoustic experiment.