DGHMesh supplies a large-scale dual-radar mmWave dataset and generalization benchmark for human mesh reconstruction, together with the mmPTM multi-radar fusion model that reports strong accuracy and cross-configuration performance.
High-resolution frequency-wavenumber spectrum analysis
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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.
citing papers explorer
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DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction
DGHMesh supplies a large-scale dual-radar mmWave dataset and generalization benchmark for human mesh reconstruction, together with the mmPTM multi-radar fusion model that reports strong accuracy and cross-configuration performance.
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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.