This paper presents a new model order selection technique for signal processing applications related to source localization or subspace orthogonal projection techniques in large dimensional regime (Random Matrix Theory) when the noise environment is Complex Elliptically Symmetric (CES) distributed, with unknown scatter matrix. The proposed method consists first in estimating the Toeplitz structure of the background covariance matrix. In a second step, after a whitening process, the eigenvalues distribution of any Maronna's M-estimators is exploited, leading to the order selection. Simulations made on different kinds of CES noise as well as analysis of real hyperspectral images demonstrate the superiority of the proposed technique compared to those of Akaike Information Criterion and the Minimum Description Length.