Order statistics approach for determining the number of sources using an array of sensors

A new approach for estimating the number of radiating, not fully correlated sources using the data received by an array of sensors is presented. The common approach is to apply information theoretic criteria, such as the minimum description length (MDL) or the Akaike information criterion (AIC), on the received data. Alternatively, we suggest to apply these criteria on the ordered eigenvalues of the sample data covariance matrix. While asymptotically, as the number of snapshots tends to infinity, the two approaches converge, we demonstrate that for any finite number of samples there exist physical conditions for which the proposed approach outperforms the traditional one. These cases are associated with spatially close sources, or with highly correlated sources, or with the case of sources with very different signal-to-noise ratio (SNR).