Source enumeration using the pdf of sample eigenvalues via information theoretic criteria

The problem of source enumeration in array processing is investigated. In an information theoretic criterion framework, we use in addition to the probability density function of observations, the probability density function of the sample eigenvalues obtained from the sample covariance matrix of the observations. Although the latter adds information to the criterion it is widely ignored by most traditional approaches. Simulations show that the significant performance gain offered by the proposed criterion in terms of correctly detecting the number of sources in some difficult situations, such as small sample sizes, low signal-to-noise power ratio, close spacing and high correlation between sources.