Artificial Neural Networks in an Inertial Measurement Unit

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU119242" target="_blank" >RIV/00216305:26220/16:PU119242 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7477431" target="_blank" >https://ieeexplore.ieee.org/document/7477431</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/RADIOELEK.2016.7477431" target="_blank" >10.1109/RADIOELEK.2016.7477431</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial Neural Networks in an Inertial Measurement Unit

Popis výsledku v původním jazyce

This paper presents an effective method combining classic data processing using a simple MEMS inertial measurement unit (IMU) and an artificial neural network (AAN) to achieve more accurate pedestrian positioning. Generally, this application based on a standard IMU without support from another system, such as satellite navigation, is characterized by poorly estimating position and orientation, wherein the positioning error grows over time. The proposed approach uses an artificial neural network, which is designed to determine the status of "what is happening" with the body of the IMU. Two possible statuses are considered. The first of these is the fact that the IMU is static, regardless of its orientation, and the second state is a man walking with an IMU placed on his body. In principal, further statuses can be added to the classification results from the ANN, e.g. jogging, driving, shaking, spinning, flying, falling etc. This paper not only presents the theoretical but also a series of experiments. It has been demonstrated that the proposed approach improves personal tracking accuracy by more than ten times compared to the application of an unaided IMU.

Název v anglickém jazyce

Artificial Neural Networks in an Inertial Measurement Unit

Popis výsledku anglicky

This paper presents an effective method combining classic data processing using a simple MEMS inertial measurement unit (IMU) and an artificial neural network (AAN) to achieve more accurate pedestrian positioning. Generally, this application based on a standard IMU without support from another system, such as satellite navigation, is characterized by poorly estimating position and orientation, wherein the positioning error grows over time. The proposed approach uses an artificial neural network, which is designed to determine the status of "what is happening" with the body of the IMU. Two possible statuses are considered. The first of these is the fact that the IMU is static, regardless of its orientation, and the second state is a man walking with an IMU placed on his body. In principal, further statuses can be added to the classification results from the ANN, e.g. jogging, driving, shaking, spinning, flying, falling etc. This paper not only presents the theoretical but also a series of experiments. It has been demonstrated that the proposed approach improves personal tracking accuracy by more than ten times compared to the application of an unaided IMU.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 26th International Conference RADIOELEKTRONIKA 2016

ISBN

978-1-5090-1673-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

176-180

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Košice

Datum konání akce

19. 4. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000383741100036