Performance analysis of a two-stage Rao detector

This paper deals with the problem of adaptive signal detection in the presence of Gaussian disturbance with unknown covariance matrix. A new two-stage Rao test detector is proposed, which is obtained by cascading a GLRT-based subspace detector (SD) and the Rao test. The statistical characterization for the proposed two-stage test statistic is provided, under both noise-only and signal-plus-noise hypotheses. The associated probability of false alarm (Pfa) and probability of detection (Pd) are derived in closed form. Performance of the proposed detector is demonstrated by simulation studies, wherein recently proposed detectors are involved for performance comparison. The results show that our detector can achieve better robustness with respect to (w.r.t.) the existing two-stage Rao test detector (AMF–RAO), and has better selectivity than the improved adaptive sidelobe blankers (WAS-ASB and KWAS-ASB) for small values of system parameters.