Iterative Maximum Likelihood based Method for Covariance and DOA Estimation in Unknown Nonuniform Noise
Yunpeng Xiong, Yu Zhang, Ke Miao · 2024
For scenarios of unknown uniform noise, an iterative optimization method based on maximum likelihood (ML) estimator for covariance and direction-of-arrival (DOA) estimation is presented in this paper. Firstly, by accomplishing the log-likelihood maximization function in an iterative optimization manner with the structured Toeplitz constraint, the noise-free covariance matrix is reconstructed. Then, with the obtained covariance matrix estimate, the DOAs are retrieved via traditional DOA estimators. Compared with the off-the-shelf DOA estimation techniques, the proposed solution for DOA estimation can not only work with no needing of the information of the source number, but also has better estimation accuracy. Numerical simulations demonstrate that the proposed solution outperforms the existing methods in terms of estimation accuracy.