[02473] Applications of Bures-Wasserstein geometry of HPD matrices to signal detection
Session Time & Room : 2C (Aug.22, 13:20-15:00) @F411
Type : Contributed Talk
Abstract : Autocovariance matrices can describe characteristic of time series data. If the data follow the stationary process, the corresponding autocovariance matrix is Hermitian positive definite (HPD). In this talk, we introduce Riemannian geometry of the HPD matrix spaces equipped with the Bures–Wasserstein (BW) metric and propose a detection method by utilizing the geodesic distance to define BW mean and median of HPD matrices. Robustness of the proposed mean and median will also be analyzed.
