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20172025
most citedSparse Bayesian Learning for DOA Estimation in Heteroscedastic Noise

3 citations · 6 across the 7 of their papers we have counts for

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5 papers · 1 filter

eess.SP2025

Near-field Anchor-free Localization using Reconfigurable Intelligent Surfaces

Srikar Sharma Sadhu, Praful D. Mankar, Santosh Nannuru

Near-field localization is expected to play a crucial role in enabling a plethora of applications under the paradigm of 6G networks. The conventional localization methods rely on c…

eess.SP2025

Real-time identification and control of influential pandemic regions using graph signal variation

Sudeepini Darapu, Subrata Ghosh, Dibakar Ghosh +2

The global spread of pandemics is facilitated by the mobility of populations, transforming localized infections into widespread phenomena. To contain it, timely identification of i…

eess.SP2025

Near-field 5D Pose Estimation using Reconfigurable Intelligent Surfaces

Srikar Sharma Sadhu, Praful D. Mankar, Santosh Nannuru

The advent of 6G is expected to enable many use cases which may rely on accurate knowledge of the location and orientation of user equipment (UE). The conventional localization met…

eess.SP20223 cited

Parametric Models for DOA Trajectory Localization

Ruchi Pandey, Santosh Nannuru

Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most localization m…

eess.SP20173 cited

Sparse Bayesian Learning for DOA Estimation in Heteroscedastic Noise

Peter Gerstoft, Santosh Nannuru, Christoph F. Mecklenbräuker +1

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stat…