A data-based enumeration technique for fully correlated signals
Presents a novel method for estimating the number of signals impinging on a uniform linear array using observed sensor data. Unlike other algorithms that apply Rissanen's minimum description length (MDL) principle to the observed data for source enumeration, this method applies it to the prediction errors of a linear model that has been fitted to an appropriate data matrix. It is a 1D method that achieves improved performance even for fully correlated signals over contemporary approaches, particularly with short data records and closely spaced signals. Asymptotic consistency is shown and substantiating simulation examples are included. >