An Improved Adaptive Radar Detector Based on Two Sets of Training Data

In this paper, we deal with the problem of detecting a point-like target in a homogeneous interference environment characterized by the presence of clutter, noise-like jamming, and radar internal noise. To this end, we assume that two sets of training data which contain different interference components are available. Within this context, we propose a two-step estimation procedure to provide an accurate estimate of the interference covariance matrix. The latter is then used to construct an adaptive detector resorting to the two-step modification of the generalized likelihood ratio test. Finally, a preliminary performance assessment demonstrates the effectiveness of the proposed method achieving better performance with respect to the other state-of-the-art detector in the case of sample starved scenarios.