Robust Maximum Likelihood Bearing Estimation in
David D. Lee, Rangasami L. Kashyap · 1992
A robust maximum likelihood (ML) direction-of- arrival (DOA) estimation method which is insensitive to out- liers and distributional uncertainties in Gaussian noise is pre- sented. The algorithm has been shown to perform much better than the Gaussian ML algorithm when the underlying noise distribution deviates even slightly from Gaussian while still performing almost as well in pure Gaussian noise. As with the Gaussian ML estimation, it is still capable of handling corre- lated signals as well as single snapshot cases. Performance of the algorithm is analyzed using our unique resolution test pro- cedure which determines whether a DOA estimation algorithm, at a given confidence level, can resolve two dominant sources with very close DOA's or not.