Performance breakdown prediction for maximum likelihood DoA estimation

“Performance breakdown ” of maximum-likelihood (ML) direction-of-arrival (DoA) estimation is analyzed. “Performance breakdown” occurs when signal-to-noise ratio (SNR) and/or training sample vol-ume fall below some threshold values and a ML set of DoA esti-mates calculated for properly detected number of sources, unavoid-ably contains an estimation “outlier”. In this paper, we propose a technique to “predict ” (i.e. identify, recognize) the underlying sce-nario and an ML set of DoA estimates, as potentially containing an outlier and specify these potential outliers. Index Terms — Parameter estimation, signal detection, maxi-mum likelihood estimation, array signal processing