Multipath Exploitation for Knowledge-Aided Adaptive Target Detection

Abstract

The authors consider the problem of multipath exploitation on adaptive radar detection of point-like targets in a multipath environment where a priori information is available. A new approach to exploit multipath returns with knowledge-aided adaptive target-detection regime is proposed. The authors model the received signal as the sum of direct-path and reflected-path return under the assumption of a zero-mean complex circular Gaussian noise with an unknown covariance matrix. The advantage of the proposed method is exploiting multipath returns with a priori knowledge of the reflecting environment, so that it has the knowledge of the reflected steering vector for a known actual direct-path steering vector. A Generalised Likelihood Ratio Test (GLRT) for the corresponding hypothesis testing problem is derived. It is shown that the devised detector also secures the Constant False Alarm Rate (CFAR) property regarding the unknown parameters of the noise. Performance comparison of the proposed detector with the existing well-known adaptive detectors is provided. It is presented that better-detection performance can be achieved by exploiting multipath with knowledge-aided adaptive radar. It is also observed that the devised detector has a small performance degradation in case of weak multipath return. © The Institution of Engineering and Technology 2019