Artificial neural network-based indoor localization system using smartphone magnetometer
| Autor: | Salah Eddine, Bouzid, Simondet, Amaury, Chargé, Pascal |
|---|---|
| Přispěvatelé: | Institut d'Électronique et des Technologies du numéRique (IETR), Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Charlier, Sandrine, Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
[SPI.OTHER]Engineering Sciences [physics]/Other
smartphone sensors [SPI.OTHER] Engineering Sciences [physics]/Other multi-output regression Indoor localization magnetic field-based localization artificial neural network. I [SPI.TRON] Engineering Sciences [physics]/Electronics [SPI.TRON]Engineering Sciences [physics]/Electronics |
| Zdroj: | IEEE International Conference on Antenna Measurements and Applications IEEE International Conference on Antenna Measurements and Applications, Nov 2021, Antibes Juan-les-Pins, France. pp.ID 154 |
| Popis: | International audience; In recent years, localization systems have becomean interesting research topic since they present a key factorfor location-based services. Although the global positioningsystem (GPS) is widely used for outdoor positioning, it doesnot provide the same accuracy in indoor environments. As aresult, many alternative indoor positioning technologies havebeen investigated to tackle this problem during the last fewyears. However, most existing approaches (e.g., Camera, WiFi,and infrared-based methods) for indoor localization mainly relyon infrastructure, which is expensive and not scalable. Today,the expansion of smartphones possessing a variety of embeddedsensors helped develop a precise indoor localization that canmeet the requirements of location-based services.This paper proposes an indoor localization system based onmagnetic field sensed via a smartphone magnetometer. Anomaliescaused by the presence of ferromagnetic materials are usedas signatures and fingerprinting to identify different locations.Accordingly, an Android application was developed to build ageomagnetic fingerprinting database in the corridor of PolytechNantes, France. 7600 signatures were stored in the database,cleaned, and standardized. 70% of data is used to train andvalidate different multi-output regression models. Extensivesimulations are conducted to find the suitable model and totune model’s hyper-parameters. Once the model is configuredand trained, remaining unseen data is used to evaluate theaccuracy of the proposed system. Obtained results demonstratethat ANN is the most accurate model with Mean Absolute Error(MAE) equals 0.13m for the studied environment. Only 8% oftesting samples have errors higher than the MAE. Moreover, theproposed indoor localization system can locate the user withoutprior knowledge of his initial position. |
| Databáze: | OpenAIRE |
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