Çoklu Yansıma Ortamlarında Geniş Menzilli Hedeflerin Uyarlanabilir Radar Tespiti

This paper discusses the adaptive detection of extended radar targets buried in Gaussian clutter, assuming a diffuse multipath environment. The target return signal from each range cell is modeled as the sum of a deterministic data vector, which includes an unknown scaling factor representing the direct path component, and a randomly distributed data vector in a Gaussian distribution with unknown covariance matrix representing multipath echoes. During the design phase, it is assumed that the primary data covariance matrix falls within the vicinity of a sample covariance matrix that is devised from the secondary data set. The paper proposes a constraint Generalized Likelihood Ratio Test (GLRT) for the adaptive detection problem of extended radar targets in diffuse multipath environments, and conducts a performance analysis comparing the developed algorithm with well-known adaptive detectors in the literature. The results and performance analysis demonstrate that the proposed approach enhances the detection performance of extended radar targets in environments with diffuse multipath. Overall, this article provides valuable insights for improving the adaptive detection of extended targets in challenging environments, with potential applications in radar and sensing technologies.