AULAs and variants optimize both sum and difference co-arrays via specific sparse-dense ULA combinations to deliver larger virtual apertures and higher degrees of freedom for non-circular source localization with reduced mutual coupling.
Three more decades in array signal processing research: An optimization and structure exploitation perspective
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SERCOM applies the Jensen-Bregman LogDet divergence on the Riemannian manifold of Hermitian positive definite matrices to achieve more robust and accurate direction-of-arrival and power estimation than conventional Euclidean covariance matching methods.
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AULAs: A Novel Family of Augmented ULAs for Enhanced Localization of Non-Circular Sources with Reduced Mutual Coupling Effects
AULAs and variants optimize both sum and difference co-arrays via specific sparse-dense ULA combinations to deliver larger virtual apertures and higher degrees of freedom for non-circular source localization with reduced mutual coupling.
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Spatial Power Estimation via Riemannian Covariance Matching
SERCOM applies the Jensen-Bregman LogDet divergence on the Riemannian manifold of Hermitian positive definite matrices to achieve more robust and accurate direction-of-arrival and power estimation than conventional Euclidean covariance matching methods.