Durbin tests for distributed target detection in deterministic subspace interference and noise

Peiqin Tang, Xinyu Peng, Hong Xu, Weijian Liu, Jun Liu, Yongliang Wang

Abstract: This paper investigates the problem of distributed target detection in the high-resolution radar system, where the target is embedded in interference and Gaussian noise. The target and interference are modeled as two subspace random signals, which are assumed to belong to different deterministic and known subspaces, but with unknown coordinates. To address this issue, several adaptive detectors are proposed resorting to the Durbin criterion tailored for both homogeneous and partially homogeneous environments. At the analysis stage, numerical experiments are conducted to evaluate the performance of the detectors from various parameter perspectives. The results indicate that the proposed Durbin-based detectors outperform their competitors in some scenarios. Furthermore, these detectors can ensure the constant false alarm rate property with respect to the unknown statistics of the noise component.