An eigenvector technique for detecting the number of emitters in a cluster

The paper introduces a new algorithm for estimating the number of sources in a cluster of closely spaced sources. The algorithm is based on consideration of the eigenvectors of the sample covariance matrix and is designated as the eigenvector detection technique (EDT). It is shown by examples that the EDT can reliably detect sources that number at lower signal-to-noise ratios (SNRs) than either the minimum description length (MDL) or Akaike information criterion (AIC) algorithms. The paper also presents a performance analysis of the EDT. Results include a “theoretical” expression for detection threshold SNR and a “theoretical” curve of probability of detection versus SNR for the technique; all analysis results show good agreement with simulation results