Convolutional-sparse-coded Dynamic Mode Decomposition and Its Application to River State Estimation
Yu Kaneko, Shogo Muramatsu, Hiroyasu Yasuda, Kiyoshi Hayasaka, Yu Otake, Shunsuke Ono, Masahiro Yukawa
Abstract
This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a data-driven method of analysis used to describe a nonlinear dynamical system with a linear time-evolution equation. Compared with existing EDMD methods, CSC-DMD has the advantage of reflecting the spatial structure of a target. As an example, the proposed method is applied to river bed shape estimation from the water surface observation. This estimation problem is reduced to sparsityaware signal restoration with a hard constraint given by the CSC-DMD prediction, where the algorithm is derived by the primal-dual splitting method. A time series set of water surface and bed shape measured through an experimental river setup is used to train and test the system. From the result, the efficacy of the proposed method is verified.
BibTeX
@inproceedings{icassp2019_convolutionalspa,
title = {Convolutional-sparse-coded Dynamic Mode Decomposition and Its Application to River State Estimation},
author = {Yu Kaneko and Shogo Muramatsu and Hiroyasu Yasuda and Kiyoshi Hayasaka and Yu Otake and Shunsuke Ono and Masahiro Yukawa},
booktitle = {ICASSP 2019},
year = {2019}
}