Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

In this letter, we employ and design the expectation--conditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm the effectiveness of the algorithm.

EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise

The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum--likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.

EM and SAGE algorithms for DOA Estimation in the Presence of Unknown Uniform Noise

The expectation-maximization (EM) and space-alternating generalized EM (SAGE) algorithms have been applied to direction of arrival (DOA) estimation in known noise. In this work, the two algorithms are proposed for DOA estimation in unknown uniform noise. Both the deterministic and stochastic signal models are considered. Moreover, a modified EM (MEM) algorithm applicable to the noise assumption is also proposed. These proposed algorithms are improved to ensure the stability when the powers of sources are unequal. After being improved, numerical results illustrate that the EM algorithm has similar convergence with the MEM algorithm and the SAGE algorithm outperforms the EM and MEM algorithms for the deterministic signal model. Furthermore, numerical results show that processing the same samples from the stochastic signal model, the SAGE algorithm for the deterministic signal model requires the fewest iterations.