Multipath Exploitation Radar With Adaptive Detection in Partially Homogeneous Environments

Abstract

The authors deal with the problem of detecting point-like targets in the presence of diffuse multipath under the assumption of a partially homogeneous Gaussian disturbance by introducing an unknown scaling factor which represents the mismatch between the noise contribution of the cell under test and the training samples. Also, they model the target echo as a superposition of direct plus multipath components where multipath returns are thought of as scattered signals from a glistening surface. Hence, multipath echoes are represented as a Gaussian distributed random vector with an unknown covariance matrix. Then, the authors derive a constrained generalised likelihood ratio test under the assumption that the primary data covariance structure is similar to the covariance matrix obtained from training samples where the degree of similarity is up to both noise scaling factor and multipath contribution. Besides, they prove that the proposed detector ensures constant false alarm rate (CFAR) property with respect to the unknown parameters. Finally, they compared the devised algorithm with the commonly used CFAR estimators. The results show that the proposed detector copes well with diffuse multipath conditions under partially homogeneous environments.