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The subspace-based techniques are widely utilized in various scientificfields, and they need accurate estimation of the signal subspace dimension. Theclassic RMT estimator for model order estimation based on random matrix theoryassumes that the noise is white Gaussian, and performs poorly in the presenceof colored noise with unknown covariance matrix. In the presence of colorednoise, the multivariate regression (MV-R) algorithm models the source detectionas a multivariate regression problem and infers the model order from thecovariance matrix of the residual error. However, the MV-R algorithm requiresthat the noise is sufficiently weaker than the signal. In order to deal withthese problems, this paper proposes a novel signal number estimation algorithmin the presence of colored noise based on the analysis of the behavior ofinformation theoretic criteria. Firstly, a first criterion is defined as theratio of the current eigenvalue and the mean of the next ones, and itsproperties is analyzed with respect to the over-modeling and under-modeling.Moreover, a second criterion is designed as the ratio of the current value andthe next value of the first criterion, and its properties is analyzed withrespect to the over-modeling and under-modeling. Then, a novel enhanced RMTestimator is proposed for signal number estimation by analyzing the detectionproperties among the signal number estimates obtained by these two criteria,the MV-R estimator and the RMT estimator to sequentially determine whether theeigenvalue being tested is arising from a signal or from noise. Finally,simulation results are presented to illustrate that the proposed enhanced RMTestimator has better estimation performance than the existing methods.

Enhanced RMT estimator for signal number estimation in the presence of colored noise