  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.
