Adaptive detection of point-like targets in environment with orthogonal rejection

This paper deals with the problem of detecting point-like targets in presence of Gaussian disturbance with unknown covariance matrix. We consider a situation where the environment is partially homogeneous, i.e., where the primary and secondary data share the same structure of the covariance matrix but different power level. Moreover, we assume that the cell under test (CUT) contains a fictitious signal orthogonal to the nominal steering vector under the null hypothesis. Under the above assumptions, we devise an adaptive detector based on the generalized likelihood ratio test (GLRT). Interestingly, it is shown that the proposed detector coincides with the well known adaptive coherence estimator (ACE). This result provides an alternative explanation of the good selectivity properties exhibited by the ACE.