Adaptive Radar Detection and Range Estimation with Oversampled Data for Partially Homogeneous Environment

Abstract

In the present letter we investigate the problem of adaptive detection and range estimation for point-like targets buried in partially homogeneous Gaussian disturbance with unknown covariance matrix. To this end, we jointly exploit the spillover of target energy to consecutive range samples and the oversampling of the received signal. In this context, we design a detector relying on the Generalized Likelihood Ratio Test (GLRT). Remarkably, the new decision scheme ensures the Constant False Alarm Rate (CFAR) property with respect to the unknown disturbance parameters. The performance analysis reveals that it can provide enhanced detection performance compared with its state-of-art counterpart while retaining accurate estimation capabilities of the target position.