GLRT Detectors for Airborne Radar Based on Knowledge-Aided and Compressive Sensing

Abstract

This paper deals with the detection problem of airborne phased array radar in known and unknown prior spectrum knowledge scenarios. In the former case, several novel knowledge-aided (KA) detectors under the generalized likelihood ratio test (GLRT) framework are proposed, e.g., two detectors based on structured clutter covariance matrix (CCM) and two step least square (TSLS) algorithm without samples. We further present another two improved KA detectors on the basis of training data. In the latter case, we develop compressive sensing (CS) detectors, e.g., Bayesian compressive sensing (BCS) detector without using samples. We further propose block sparse Bayesian compressive sensing (BSBCS) detector with training data available. Finally, we compare the several proposed detectors with each other and numerical results indicate that the proposed detectors exhibit more significant performances than the traditional detector.