  This paper presents a series of user parameter-free iterative Sparse
Asymptotic Minimum Variance (SAMV) approaches for array processing applications
based on the asymptotically minimum variance (AMV) criterion. With the
assumption of abundant snapshots in the direction-of-arrival (DOA) estimation
problem, the signal powers and noise variance are jointly estimated by the
proposed iterative AMV approach, which is later proved to coincide with the
Maximum Likelihood (ML) estimator. We then propose a series of power-based
iterative SAMV approaches, which are robust against insufficient snapshots,
coherent sources and arbitrary array geometries. Moreover, to overcome the
direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML
(SAMV-SML) approaches are derived by explicitly minimizing a closed form
stochastic ML cost function with respect to one scalar parameter, eliminating
the need of any additional grid refinement techniques. To assist the
performance evaluation, approximate solutions to the SAMV approaches are also
provided at high signal-to-noise ratio (SNR) and low SNR, respectively.
Finally, numerical examples are generated to compare the performance of the
proposed approaches with existing approaches.
