A DATA-BASED ENUMERATION TECHNIQUE FOR FULLY CORRELATED SIGNALS

We present a novel method for estimating the number of signals impingi ng on a uniform linear array using observed sensor data. Unlike other algorithms that apply Rissanen's minimum description length (MDL) prin ciple 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 1-D method that achieves improved performance even for fully correlated signals over contemporary appro aches, particularly with short data records and closely spaced signals . Asymptotic consistency is shown and substantiating simulation exampl es are included.