Abstract
This paper presents a pre-processing procedure associated with a Riemannian geometry method to carry out a target detection in a radar system. Firstly, the sample data in each range cell in one coherent processing interval is modeled as a Hermitian positive-definite (HPD) matrix. All these matrices constitute a complex Riemannian manifold. Each point on this manifold is an HPD matrix. Then, the new radar observation data for each range cell is an HPD matrix, and a pre-processing procedure, called the weighted averaging filter, is imposed on these HPD matrices. The covariance matrix in each range cell is replaced by a weighted average of covariance matrices of its surrounding cells. The decision rule is made by comparing the distance between the Riemannian mean estimated by the covariance matrices of neighboring locations and the covariance matrix of cell under test with a threshold. The superiority of the proposed algorithm is verified by numerical experiments.
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