Upgrade Methods for Stratified Sensor Network Self-Calibration

M. Larsson; G. Flood; M. Oskarsson; K. Astrom

doi:10.1109/ICASSP40776.2020.9054025

Upgrade Methods for Stratified Sensor Network Self-Calibration

M. Larsson, G. Flood, M. Oskarsson, K. Astrom

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

Estimating receiver and sender positions is often solved using a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. The second step can be seen as an affine upgrade. This affine upgrade is the focus of this paper. In the paper new efficient algorithms for solving for the upgrade parameters using minimal data are presented. It is also shown how to combine such solvers as initial estimates, either directly or after a hypothesis and test step, in optimization of likelihood. The system is verified on both real and synthetic data.

Originalspråk	engelska
Titel på värdpublikation	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Förlag	IEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor	4851-4855
Antal sidor	5
ISBN (elektroniskt)	9781509066315
DOI	https://doi.org/10.1109/ICASSP40776.2020.9054025
Status	Published - 2020
Evenemang	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spanien Varaktighet: 2020 maj 4 → 2020 maj 8

Publikationsserier

Namn	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volym	2020-May
ISSN (tryckt)	1520-6149

Konferens

Konferens	2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Land/Territorium	Spanien
Ort	Barcelona
Period	2020/05/04 → 2020/05/08

Ämnesklassifikation (UKÄ)

Kommunikationssystem

Tillgång till dokument

10.1109/ICASSP40776.2020.9054025

2 Doktorsavhandling (sammanläggning)

Localization using Distance Geometry: Minimal Solvers and Robust Methods for Sensor Network Self-Calibration
Larsson, M., 2022, Lund: Mathematics Centre for Mathematical Sciences Lund University Lund. 200 s.
Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)

Öppen tillgång
Fil
Mapping and Merging Using Sound and Vision: Automatic Calibration and Map Fusion with Statistical Deformations
Flood, G., 2021 dec. 10, Lund University / Centre for Mathematical Sciences /LTH. 238 s.
Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)

Öppen tillgång
Fil

Citera det här

Larsson, M., Flood, G., Oskarsson, M., & Astrom, K. (2020). Upgrade Methods for Stratified Sensor Network Self-Calibration. I 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (s. 4851-4855). Artikel 9054025 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054025

Larsson, M. ; Flood, G. ; Oskarsson, M. et al. / Upgrade Methods for Stratified Sensor Network Self-Calibration. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. s. 4851-4855 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{6c1a49b4124542c090593c86b9141ba5,

title = "Upgrade Methods for Stratified Sensor Network Self-Calibration",

abstract = "Estimating receiver and sender positions is often solved using a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. The second step can be seen as an affine upgrade. This affine upgrade is the focus of this paper. In the paper new efficient algorithms for solving for the upgrade parameters using minimal data are presented. It is also shown how to combine such solvers as initial estimates, either directly or after a hypothesis and test step, in optimization of likelihood. The system is verified on both real and synthetic data.",

keywords = "Calibration, Minimal Problems, RANSAC, Time-difference-of-arrival, Time-of-arrival",

author = "M. Larsson and G. Flood and M. Oskarsson and K. Astrom",

year = "2020",

doi = "10.1109/ICASSP40776.2020.9054025",

language = "English",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

pages = "4851--4855",

booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",

address = "United States",

note = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",

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Larsson, M , Flood, G , Oskarsson, M & Astrom, K 2020, Upgrade Methods for Stratified Sensor Network Self-Calibration. i 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings., 9054025, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2020-May, IEEE - Institute of Electrical and Electronics Engineers Inc., s. 4851-4855, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, Spanien, 2020/05/04. https://doi.org/10.1109/ICASSP40776.2020.9054025

Upgrade Methods for Stratified Sensor Network Self-Calibration. / Larsson, M.; Flood, G.; Oskarsson, M. et al.
2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. IEEE - Institute of Electrical and Electronics Engineers Inc., 2020. s. 4851-4855 9054025 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May).

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

TY - GEN

T1 - Upgrade Methods for Stratified Sensor Network Self-Calibration

AU - Larsson, M.

AU - Flood, G.

AU - Oskarsson, M.

AU - Astrom, K.

PY - 2020

Y1 - 2020

N2 - Estimating receiver and sender positions is often solved using a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. The second step can be seen as an affine upgrade. This affine upgrade is the focus of this paper. In the paper new efficient algorithms for solving for the upgrade parameters using minimal data are presented. It is also shown how to combine such solvers as initial estimates, either directly or after a hypothesis and test step, in optimization of likelihood. The system is verified on both real and synthetic data.

AB - Estimating receiver and sender positions is often solved using a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. The second step can be seen as an affine upgrade. This affine upgrade is the focus of this paper. In the paper new efficient algorithms for solving for the upgrade parameters using minimal data are presented. It is also shown how to combine such solvers as initial estimates, either directly or after a hypothesis and test step, in optimization of likelihood. The system is verified on both real and synthetic data.

KW - Calibration

KW - Minimal Problems

KW - RANSAC

KW - Time-difference-of-arrival

KW - Time-of-arrival

U2 - 10.1109/ICASSP40776.2020.9054025

DO - 10.1109/ICASSP40776.2020.9054025

M3 - Paper in conference proceeding

AN - SCOPUS:85089235732

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 4851

EP - 4855

BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 4 May 2020 through 8 May 2020

ER -

Larsson M , Flood G , Oskarsson M , Astrom K. Upgrade Methods for Stratified Sensor Network Self-Calibration. I 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings. IEEE - Institute of Electrical and Electronics Engineers Inc. 2020. s. 4851-4855. 9054025. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP40776.2020.9054025

Upgrade Methods for Stratified Sensor Network Self-Calibration

Sammanfattning

Publikationsserier

Konferens

Ämnesklassifikation (UKÄ)

Tillgång till dokument

Fingeravtryck

Forskningsoutput

Localization using Distance Geometry: Minimal Solvers and Robust Methods for Sensor Network Self-Calibration

Mapping and Merging Using Sound and Vision: Automatic Calibration and Map Fusion with Statistical Deformations

Citera det här