The authors propose a computationally efficient scheme for estimating the spatial (location) and spectral parameters of multiple-source signals using passive array data. The source signals are modeled as possibly correlated narrowband/wideband Gaussian random processes. The authors also allow sensor-to-sensor noise correlation. The proposed algorithm is optimal in the sense that it converges monotonically to the maximum-likelihood (ML) estimates (or, at least, to a stationary point of the likelihood function) of the unknown spatial and spectral parameters.>

A method is presented for evaluating the log-likelihood gradient (score), the Hessian, and the Fisher information matrix of the parameters of linear dynamic stochastic systems. The method incorporates the optimal Kalman smoothing equations and is therefore ideal for simultaneous state estimation and parameter identification. The result can be used for efficient implementation of gradient-based algorithms for maximum-likelihood identification of the unknown system parameters and for assessing the mean-square estimation accuracy.>

The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and performs intermediate steps at some iterations. The authors consider the problem of parameter identification of a continuous-time linear dynamic system given discrete-time observations, and show that the proposed algorithm accelerates the convergence of the EM algorithm and simplifies the computations involved.>