DOA estimation performance breakdown: A new approach to prediction and cure

The well-known performance breakdown of subspace-based parameter estimation methods is usually attributed to a specific property of the technique, namely “subspace swap”. In this paper, we derive the lower bound for the maximum likelihood ratio (LR), and use it as a simple data-based indicator to determine whether or not any set of estimates could be treated as a maximum likelihood (ML) set. We demonstrate that in those cases where the performance breakdown is subspace specific, this LR analysis provides reliable identification of whether or not “subspace swap” has actually occurred. We also demonstrate that by proper LR maximisation, we can extend the range of signal-to-noise ratio (SNR) values and/or number of data samples wherein accurate parameter estimates are produced. Yet, when the SNR and/or sample size falls below a certain limit for a given scenario, we show that ML estimation suffers from a discontinuity in the parameter estimates, a phenomenon that cannot be eliminated within the ML paradigm.