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

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