A Persymmetric Adaptive Detection Framework

In this paper, we propose a radar adaptive detection framework that leverages persymmetric prior knowledge (PPK) of the clutter covariance matrix (CCM) to improve both the accuracy of environment model recognition and the performance of target detection. The framework first employs the Akaike information criterion (AIC), a model order selection (MOS) technique, to identify the optimal environmental model. It then selects the most suitable persymmetric adaptive detector for target detection based on the identified model. Compared to conventional methods that do not utilize PPK, our approach demonstrates superior performance in both environmental recognition and target detection.