In conventional radar signal processing, a structured model for the target response is considered while characterizing the clutter and interference using the covariance matrix of the data distribution. The channel matrix-based model, on the other hand, models the target and clutter returns in the same way, i.e., responses to corresponding channels. This results in a more versatile model which can incorporate many scenarios. In this paper, we derive the generalized likelihood ratio test (GLRT) statistics for channel matrix-based MIMO radar data model under the assumption of complex multivariate elliptically symmetric (CMES) data distribution. We consider both known and unknown covariance matrices of the waveform-independent colored noise (WICN). For the known covariance case, the GLRT statistic follows a chi-square distribution, while for the unknown covariance case, it aligns with Wilks' lambda distribution. We also examine the GLRT statistic for the known WICN covariance case when the maximum likelihood estimate of the covariance matrix is used to replace the true covariance matrix, showing that it matches the Bartlett-Nanda-Pillai trace statistic under the null hypothesis and follows a non-central Lawley-Hotelling T 2 0 distribution under the alternative hypothesis. Asymptotically, all derived statistics converge to the known covariance case. Through Monte Carlo simulations and the saddle point approximation method, we generate receiver operating characteristic (ROC) curves for a simple numerical example, supplemented by experimental results and high-fidelity simulations.