Symmetric spectrum detection in the presence of partially homogeneous environment

In this paper, we deal with the problem of detecting the signal of interest in the presence of Gaussian disturbance with symmetric spectrum and assuming that the cell under test (primary data) and the training samples (secondary data) share the same covariance matrix up to an unknown power scaling factor. Moreover, we exploit the symmetric spectral property of the disturbance to transfer the binary hypothesis testing problem from the complex to the real domain and derive an adaptive detector relying on the two-step Generalized Likelihood Ratio Test design procedure. A preliminary performance assessment, conducted by Monte Carlo simulation, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterpart which ignores the spectrum symmetry.