1 research outputs found
On the Resolution Probability of Conditional and Unconditional Maximum Likelihood DoA Estimation
After decades of research in Direction of Arrival (DoA) estimation, today
Maximum Likelihood (ML) algorithms still provide the best performance in terms
of resolution capabilities. At the cost of a multidimensional search, ML
algorithms achieve a significant reduction of the outlier production mechanism
in the threshold region, where the number of snapshots per antenna and/or the
signal to noise ratio (SNR) are low. The objective of this paper is to
characterize the resolution capabilities of ML algorithms in the threshold
region. Both conditional and unconditional versions of the ML algorithms are
investigated in the asymptotic regime where both the number of antennas and the
number of snapshots are large but comparable in magnitude. By using random
matrix theory techniques, the finite dimensional distributions of both cost
functions are shown to be Gaussian distributed in this asymptotic regime, and a
closed form expression of the corresponding asymptotic covariance matrices is
provided. These results allow to characterize the asymptotic behavior of the
resolution probability, which is defined as the probability that the cost
function evaluated at the true DoAs is smaller than the values that it takes at
the positions of the other asymptotic local minima