Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on  Embedded Ultra-Wideband Devices

Markus Gallacher; Carlo Alberto Boano; Michael Stocker; Kay Uwe Römer

Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices

Markus Gallacher, Carlo Alberto Boano, Michael Stocker, Kay Uwe Römer

Institut für Technische Informatik (4480)

Publikation: Konferenzbeitrag › Poster › Begutachtung

Abstract

We study the ranging error classification and mitigation capabilities of machine learning models used in ultrawideband systems. This is relevant, as distance estimates in non-line-of-sight (NLOS) conditions can be off by several
meters, which may severely compromise the performance of applications that require location awareness. Our ultimate goal is to optimize the size of a convolutional neural network (CNN) used for classifying and mitigating ranging errors such that it can run on constrained embedded devices without affecting its performance. To this end, we present an optimized CNN implementation that, in contrast to resourcehungry machine learning models requiring hundreds of kB
of memory, can classify and mitigate NLOS conditions with 12 kB of RAM and 75 kB of ROM.

Originalsprache	englisch
Publikationsstatus	Veröffentlicht - Okt. 2022
Veranstaltung	19th International Conference on Embedded Wireless Systems and Networks: EWSN 2022 - Linz, Linz, Österreich Dauer: 3 Okt. 2022 → 5 Okt. 2022 https://ewsn2022.jku.at/

Konferenz

Konferenz	19th International Conference on Embedded Wireless Systems and Networks
Kurztitel	EWSN 2022
Land/Gebiet	Österreich
Ort	Linz
Zeitraum	3/10/22 → 5/10/22
Internetadresse	https://ewsn2022.jku.at/

Fields of Expertise

Information, Communication & Computing

Zugriff auf Dokument

https://ewsn2022.pro2future.at/paper/posters+demos/ewsn22posters-final24.pdf

Intelligent & Networked Embedded Systems
Boano, C. A., Römer, K. U., Schuß, M., Cao, N., Saukh, O., Hofmann, R., Stocker, M., Schuh, M. P., Papst, F., Salomon, E., Brunner, H., Gallacher, M., Mohamed Hydher, M. H., Wang, D., Corti, F., Krisper, M., Basic, F. & Petrovic, K.
1/09/13 → …
Projekt: Arbeitsgebiet

Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices
Markus Gallacher (Redner/in)
3 Okt. 2022 → 5 Okt. 2022
Aktivität: Vortrag oder Präsentation › Posterpräsentation › Science to science

Dieses zitieren

Gallacher, M., Boano, C. A., Stocker, M., & Römer, K. U. (2022). Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices. Postersitzung präsentiert bei 19th International Conference on Embedded Wireless Systems and Networks, Linz, Österreich. https://ewsn2022.pro2future.at/paper/posters+demos/ewsn22posters-final24.pdf

Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices. / Gallacher, Markus ; Boano, Carlo Alberto ; Stocker, Michael et al.
2022. Postersitzung präsentiert bei 19th International Conference on Embedded Wireless Systems and Networks, Linz, Österreich.

Publikation: Konferenzbeitrag › Poster › Begutachtung

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title = "Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices",

abstract = "We study the ranging error classification and mitigation capabilities of machine learning models used in ultrawideband systems. This is relevant, as distance estimates in non-line-of-sight (NLOS) conditions can be off by severalmeters, which may severely compromise the performance of applications that require location awareness. Our ultimate goal is to optimize the size of a convolutional neural network (CNN) used for classifying and mitigating ranging errors such that it can run on constrained embedded devices without affecting its performance. To this end, we present an optimized CNN implementation that, in contrast to resourcehungry machine learning models requiring hundreds of kBof memory, can classify and mitigate NLOS conditions with 12 kB of RAM and 75 kB of ROM.",

author = "Markus Gallacher and Boano, {Carlo Alberto} and Michael Stocker and R{\"o}mer, {Kay Uwe}",

year = "2022",

month = oct,

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note = "19th International Conference on Embedded Wireless Systems and Networks : EWSN 2022, EWSN 2022 ; Conference date: 03-10-2022 Through 05-10-2022",

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TY - CONF

T1 - Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices

AU - Gallacher, Markus

AU - Boano, Carlo Alberto

AU - Stocker, Michael

AU - Römer, Kay Uwe

PY - 2022/10

Y1 - 2022/10

N2 - We study the ranging error classification and mitigation capabilities of machine learning models used in ultrawideband systems. This is relevant, as distance estimates in non-line-of-sight (NLOS) conditions can be off by severalmeters, which may severely compromise the performance of applications that require location awareness. Our ultimate goal is to optimize the size of a convolutional neural network (CNN) used for classifying and mitigating ranging errors such that it can run on constrained embedded devices without affecting its performance. To this end, we present an optimized CNN implementation that, in contrast to resourcehungry machine learning models requiring hundreds of kBof memory, can classify and mitigate NLOS conditions with 12 kB of RAM and 75 kB of ROM.

AB - We study the ranging error classification and mitigation capabilities of machine learning models used in ultrawideband systems. This is relevant, as distance estimates in non-line-of-sight (NLOS) conditions can be off by severalmeters, which may severely compromise the performance of applications that require location awareness. Our ultimate goal is to optimize the size of a convolutional neural network (CNN) used for classifying and mitigating ranging errors such that it can run on constrained embedded devices without affecting its performance. To this end, we present an optimized CNN implementation that, in contrast to resourcehungry machine learning models requiring hundreds of kBof memory, can classify and mitigate NLOS conditions with 12 kB of RAM and 75 kB of ROM.

M3 - Poster

T2 - 19th International Conference on Embedded Wireless Systems and Networks

Y2 - 3 October 2022 through 5 October 2022

ER -

Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices

Abstract

Konferenz

Fields of Expertise

Zugriff auf Dokument

Fingerprint

Projekte

Intelligent & Networked Embedded Systems

Aktivitäten

Poster: Towards NLOS Ranging Error Detection and Mitigation using Machine Learning on Embedded Ultra-Wideband Devices

Dieses zitieren