Blind source separation of spatio-temporal mixed signals using time-frequency analysis

Ashino, Ryuichi; Mandai, Takeshi; Morimoto, Akira; Sasaki, Fumio

doi:10.1080/00036810902767524

Cited by 19 publications

(3 citation statements)

References 21 publications

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“…In addition, the concepts of symmetry and separability of the model are introduced and the connections to the space-time linear model of coregionalization and to the classical principal component analysis are drawn. It is also worth pointing out that the proposed generalization diverges from the existing BSS methods for space-time data (Douglas et al 2007;Ashino et al 2009;de Jesu ´s Nun ˜o Ayo ´n et al 2018). All these methods work in a very different setting with respect to the one considered hereafter.…”

Section: Introductionmentioning

confidence: 81%

Blind recovery of sources for multivariate space-time random fields

Muehlmann

Iaco

Nordhausen

2022

Stoch Environ Res Risk Assess

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With advances in modern worlds technology, huge datasets that show dependencies in space as well as in time occur frequently in practice. As an example, several monitoring stations at different geographical locations track hourly concentration measurements of a number of air pollutants for several years. Such a dataset contains thousands of multivariate observations, thus, proper statistical analysis needs to account for dependencies in space and time between and among the different monitored variables. To simplify the consequent multivariate spatio-temporal statistical analysis it might be of interest to detect linear transformations of the original observations that result in straightforward interpretative, spatio-temporally uncorrelated processes that are also highly likely to have a real physical meaning. Blind source separation (BSS) represents a statistical methodology which has the aim to recover so-called latent processes, that exactly meet the former requirements. BSS was already successfully used in sole temporal and sole spatial applications with great success, but, it was not yet introduced for the spatio-temporal case. In this contribution, a reasonable and innovative generalization of BSS for multivariate space-time random fields (stBSS), under second-order stationarity, is proposed, together with two space-time extensions of the well-known algorithms for multiple unknown signals extraction (stAMUSE) and the second-order blind identification (stSOBI) which solve the formulated problem. Furthermore, symmetry and separability properties of the model are elaborated and connections to the space-time linear model of coregionalization and to the classical principal component analysis are drawn. Finally, the usefulness of the new methods is shown in a thorough simulation study and on a real environmental application.

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Section: Introductionmentioning

confidence: 81%

Blind recovery of sources for multivariate space-time random fields

Muehlmann

Iaco

Nordhausen

2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…The simplest strategy for blind image separation is the following Strategy 3, which directly applies the one dimen sional blind source separation method proposed in § 3.2 of [3]. The key idea of this one dimensional blind source sep aration method is the following: (ii) App ly the one dimensional blind source separation method explained in § 3.2 of [3] to the reshaped vec tors.…”

Section: Blind Image Separationmentioning

confidence: 99%

“…The key idea of this one dimensional blind source sep aration method is the following: (ii) App ly the one dimensional blind source separation method explained in § 3.2 of [3] to the reshaped vec tors.…”

Section: Blind Image Separationmentioning

confidence: 99%