Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm

Yuji Saito; Taku Nonomura; Koki Nankai; Keigo Yamada; Keisuke Asai; Yasuo Sasaki; Daisuke Tsubakino

doi:10.1109/LSENS.2020.2999186

Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm

Yuji Saito, Taku Nonomura, Koki Nankai, Keigo Yamada, Keisuke Asai, Yasuo Sasaki, Daisuke Tsubakino

ENG - Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

22 Citations (Scopus)

Abstract

A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

Original language	English
Article number	9105114
Journal	IEEE Sensors Letters
Volume	4
Issue number	7
DOIs	https://doi.org/10.1109/LSENS.2020.2999186
Publication status	Published - 2020 Jul

Keywords

Sensor signal processing
data processing
greedy algorithm
sensor placement
vector-measurement

ASJC Scopus subject areas

Electrical and Electronic Engineering
Instrumentation

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10.1109/LSENS.2020.2999186

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title = "Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm",

abstract = "A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors. ",

keywords = "Sensor signal processing, data processing, greedy algorithm, sensor placement, vector-measurement",

author = "Yuji Saito and Taku Nonomura and Koki Nankai and Keigo Yamada and Keisuke Asai and Yasuo Sasaki and Daisuke Tsubakino",

note = "Funding Information: This work was supported by JST CREST under Grant JPMJCR1763, Japan. Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2020",

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language = "English",

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AU - Saito, Yuji

AU - Nonomura, Taku

AU - Nankai, Koki

AU - Yamada, Keigo

AU - Asai, Keisuke

AU - Sasaki, Yasuo

AU - Tsubakino, Daisuke

PY - 2020/7

Y1 - 2020/7

N2 - A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

AB - A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this letter. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

KW - Sensor signal processing

KW - data processing

KW - greedy algorithm

KW - sensor placement

KW - vector-measurement

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JO - IEEE Sensors Letters

JF - IEEE Sensors Letters

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M1 - 9105114

ER -

Data-Driven Vector-Measurement-Sensor Selection Based on Greedy Algorithm

Abstract

Keywords

ASJC Scopus subject areas

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