  Optimal Maximum Likelihood (ML), narrow-band direction finding cannot be
easily initialized in coherent and low signal to noise ratio environments.
Sparse under-determined solvers are considered as viable solutions to this
problem, since they drastically reduce the dimensionality of the search space
by exploiting the array model sparseness. However, because of quantized
locations, conventional sparse solvers present some ambiguity problems. In this
work, we propose a novel boosting scheme for ML-type estimators, referred to as
Parallel BOOSTer (P-BOOST), where a set of generalized MUSIC solutions provides
pre-estimates of the directions and the number of coherent paths for arbitrary
sensor array geometry and noise covariance. P-BOOST delivers improved and
reliable coarse parameter estimates to a further ML or sparse optimization
stage even in coherent and/or high noise scenarios. Moreover, its dataflow is
highly parallel, which is essential in foreseen remote sensing and
telecommunication applications and fully justifies its acronym.
