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Ensembling Neural Networks for User’s Indoor Localization Using magnetic field data from Smartphones

Computers, Materials & Continua 2021年第8期68卷 2597-2620页

作者： Imran Ashraf Soojung Hur Yousaf Bin Zikria Yongwan Park Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data *** use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field *** the same vein,smaller lengths of magnetic field data decrease the localization accuracy *** current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the *** to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this *** of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG *** are performed during different times of the day to analyze the impact of time *** indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization *** comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches.

关键词： Indoor localization magnetic field data long short term memory network data normalization gated recurrent unit network deep learning

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