  Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA)
estimation technique in array signal processing. It is a parametric method that
jointly estimates signal and instrument noise by maximum likelihood, achieving
excellent statistical performance. Some drawbacks are the computational
overhead as well as the limitation to a point-source data model with fewer
sources than sensors. In this work, we propose a Sieved Maximum Likelihood
(SiML) method. It uses a general functional data model, allowing an
unrestricted number of arbitrarily-shaped sources to be recovered. To this end,
we leverage functional analysis tools and express the data in terms of an
infinite-dimensional sampling operator acting on a Gaussian random function. We
show that SiML is computationally more efficient than traditional SML,
resilient to noise, and results in much better accuracy than spectral-based
methods.
