Subspace-based direction of arrival estimation in colored ambient noise environments

Abstract A subspace-based direction of arrival (DOA) estimation method in colored ambient noise environments with low computational complexity is proposed. The colored ambient noise is first modeled by a harmonic noise model and the array covariance matrix is parameterized with respect to the signal subspace and unknown parameters in the noise model. The signal subspace and the noise model parameters can be estimated in alternating iterations by minimizing the negative log-likelihood function. To avoid nonlinear equations while solving the minimization problem, a large-sample approximate solution is obtained by replacing the nonlinear term, i.e. the inverse of the array covariance matrix, with the inverse of the sample covariance matrix. Then, the DOA is estimated based on the subspace orthogonality. To exhibit the performance of the proposed method, the maximum likelihood (ML) based algorithm and the sparse-based algorithm are compared. Simulation results show that the proposed method can obtain a similar estimation accuracy to the ML-based algorithm and a better computational efficiency than the compared algorithms.