  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.
