Cramer--Rao bound of spatio-temporal linear pre-processing in parameter estimation from sensor array
Signal Processing - Special issue on independent components analysis and beyond
EURASIP Journal on Applied Signal Processing
Robust and computationally efficient signal-dependent method for joint DOA and frequency estimation
EURASIP Journal on Advances in Signal Processing
Multidimensional Systems and Signal Processing
Localization of narrow-band sources in unknown spatially correlated noise
EURASIP Journal on Advances in Signal Processing
| Hi-index | 35.68 |
This paper is devoted to the maximum likelihood estimation of multiple sources in the presence of unknown noise. With the spatial noise covariance modeled as a function of certain unknown parameters, e.g., an autoregressive (AR) model, a direct and systematic way is developed to find the exact maximum likelihood (ML) estimates of all parameters associated with the direction finding problem, including the direction-of-arrival (DOA) angles Θ, the noise parameters α, the signal covariance Φs, and the noise power σ2. We show that the estimates of the linear part of the parameter set Φs and σ2 can be separated from the nonlinear parts Θ and α. Thus, the estimates of Φs and σ2 become explicit functions of Θ and α. This results in a significant reduction in the dimensionality of the nonlinear optimization problem. Asymptotic analysis is performed on the estimates of Θ and α, and compact formulas are obtained for the Cramer-Rao bounds (CRB's). Finally, a Newton-type algorithm is designed to solve the nonlinear optimization problem, and simulations show that the asymptotic CRB agrees well with the results from Monte Carlo trials, even for small numbers of snapshots