Adaptive Detection of Point-Like Targets in Partially Homogeneous Clutter With Symmetric Spectrum

In this correspondence, we deal with the problem of detecting the signal of interest in the presence of Gaussian clutter with symmetric spectrum. In particular, we consider the so-called partially homogeneous environment (PHE), where the cell under test and the training samples, share the same covariance matrix up to an unknown power scaling factor. At the design stage, we exploit the spectral properties of the clutter to transfer the data from the complex to the real domain. Then, we derive two adaptive detectors relying on the Rao test and a suitable modification of the generalized likelihood ratio test. The performance assessments, conducted on both simulated data and real recorded datasets demonstrate the effectiveness of the newly proposed detectors compared with the traditional state-of-the-art counterparts, which ignore the spectrum symmetry. Finally, they can ensure better detection performance than the existing symmetric spectrum detector derived for PHE.