Extended Kalman Filtering with Low-Rank Tensor Networks for MIMO Volterra System Identification

Kim Batselier, Ching Yun Ko, Ngai Wong

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
25 Downloads (Pure)


This article reformulates the multiple-input-multiple-output Volterra system identification problem as an extended Kalman filtering problem. This reformulation has two advantages. First, it results in a simplification of the solution compared to the Tensor Network Kalman filter as no tensor filtering equations are required anymore. The second advantage is that the reformulation allows to model correlations between the parameters of different multiple-input-single-output Volterra systems, which can lead to better accuracy. The curse of dimensionality in the exponentially large parameter vector and covariance matrix is lifted through the use of low-rank tensor networks. The computational complexity of our tensor network implementation is compared to the conventional implementation and numerical experiments demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings of the IEEE 58th Conference on Decision and Control, CDC 2019
ISBN (Electronic)978-1-7281-1398-2
Publication statusPublished - 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: 11 Dec 201913 Dec 2019


Conference58th IEEE Conference on Decision and Control, CDC 2019

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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