Adaptive Detection with Diffuse Multipath Exploitation in Partially Homogeneous Environments

Abstract

In this paper, we deal with the problem of detecting point-like targets in the presence of multipath under the assumption of a partially homogeneous Gaussian disturbance with unknown covariance matrix. Therefore, we introduce an unknown scaling factor which represents the mismatch between the noise covariance matrices of test and training signals. Besides, we model the target echo as a combination of direct and multipath components where multipath echoes are thought of as scattered signals from a glistening surface which is referred to as diffuse multipath environment. Hence, the total multipath return is also represented as a Gaussian distributed random vector with an unknown covariance matrix. At the design stage, we construct a constrained Generalized Likelihood Ratio Test (GLRT) by assuming that the total primary data covariance structure, in the target present case, resembles to the covariance matrix obtained from secondary data up to a degree (related to noise scaling factor and multipath contribution). Finally, at the analysis stage, we compared the developed algorithm with the existing solutions available in the open literature. The results highlight that the new detector copes well with severe multipath conditions and has considerable scale-invariance.