1 2 3 4 5 6 7 8

Finding the number of signals is crucial to parametric direction-of-arrival (DOA) estimation methods such as MUSIC and ESPRIT. In challenging scenarios such as low signal-to-noise ratio (SNR) and/or presence of closely-spaced sources, only part of the parameters can be accurately estimated while others cannot. The number of former estimates is termed as the effective model order (EMO). We first propose a procedure to determine the EMO via Monte Carlo simu-lation. Ideally an order selection rule should return a source number estimate equal to EMO, since using an overestimated signal number larger than the EMO in a parameter estimator introduces inaccurate parameter estimates, which is a waste of resources in some appli-cations, while using an underestimate renders some strong signals being treated as noise, which causes an accuracy loss in their param-eter estimates. We propose to combine an under-enumerator with an over-enumerator for accurate parameter estimation in the thresh-old region. Simulations results using the combination of the Baysian information criterion with Akaike information criterion in ESPRIT show that our proposal retains the benefit of the under-enumerators with only accurate estimates while remarkably improves the estima-tion accuracy. Index Terms — Order selection, parameter estimation, threshold region, effective model order, joint detection and estimation, array processing 1