Robust maximum likelihood bearing estimation in contaminated Gaussian noise

Presents a maximum likelihood (ML) direction-of-arrival (DOA) estimation algorithm which is robust against outliers and distributional uncertainties in the Gaussian noise. The algorithm performs much better than the Gaussian ML algorithm when the underlying noise distribution deviates from the assumed Gaussian while still performing almost as well in the pure Gaussian noise. As with the Gaussian ML estimation, it is capable of handling coherent signals as well as single snapshot cases. The authors analyze the performance of the new algorithm using the variance expression derived from influence function (IF), and then present a resolution test procedure for determining whether a given DOA estimation algorithm can resolve two dominant sources with very close DOAs for a given confidence level. Using the test, one can also check the presence of the reflected path having its DOA exactly the negative of the direct path DOA.<>