Cascaded Detector for Range-Spread Target in Non-Gaussian Clutter

Abstract

The work presented here addresses adaptive range-spread target detection in spherically invariant random vector (SIRV) clutter based on two-step generalized likelihood ratio test (GLRT) design procedure. Firstly, with known normalized clutter covariance matrix (NCCM), a robust detector with constant false alarm rate (CFAR) property is proposed, which cascades a detector based on the GLRT for target scatterer in each range cell and two binary integrators. Subsequently, the optimum parameters for the cascaded detector are obtained. Furthermore, by replacing the ideal NCCM with the approximate maximum likelihood (ML) estimated matrix based on secondary data only, the corresponding adaptive detector with approximate CFAR property is also devised. Moreover, the formulas relating the probability of false alarm to the detection threshold are deduced. The performance assessment conducted by Monte Carlo simulation confirms the effectiveness and robustness of the proposed detectors.