Adaptive high-degree cubature Kalman filter in the presence of unknown measurement noise covariance matrix

Autor: Hong Xu, Huadong Yuan, Keqing Duan, Wenchong Xie, Yongliang Wang

Publikováno v: The Journal of Engineering (2019)

Here, the authors address the state estimation problem of non-linear systems in the presence of unknown measurement noise (MN) covariance matrix. Recently, a high-degree cubature Kalman filter (HCKF) has been successfully used in the non-linear-state

Externí odkaz: https://doaj.org/article/2a87acd38cdd4a4eb627a0448612cade

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Rectangular R-transform as the limit of rectangular spherical integrals

Autor: Florent Benaych-Georges

Publikováno v: Journal of Theoretical Probability
Journal of Theoretical Probability, Springer, 2011, 24 (4), pp.969-987
Journal of Theoretical Probability, 2011, 24 (4), pp.969-987. ⟨10.1007/s10959-011-0362-7⟩

In this paper, we connect rectangular free probability theory and spherical integrals. In this way, we prove the analogue, for rectangular or square non-Hermitian matrices, of a result that Guionnet and Maida proved for Hermitian matrices in 2005. Mo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85bfe7bd37235cdfc1a77f6712b3ddbb
https://hal.archives-ouvertes.fr/hal-00652298

Large deviations for weighted empirical mean with outliers

Autor: Sandrine Péché, Jamal Najim, Mylène Maïda

We study in this article large deviations for the empirical mean of iid random vectors with some deterministic weights, whose empirical measure weakly converges to some compactly support probability distribution. The scope of this paper is to study t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bbd78c37e7e66a905ad2641fff24878