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Related Experiment Video

Updated: Jul 9, 2025

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

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Published on: February 19, 2017

Improving Indoor Pedestrian Dead Reckoning for Smartphones under Magnetic Interference Using Deep Learning.

Ping Zhu^1,2, Xuexiang Yu^1,2,3, Yuchen Han^2,3

¹School of Geospatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This study presents a novel method using convolutional neural networks (CNNs) and support vector machines (SVMs) to detect magnetic interference in smartphone sensors. The approach significantly improves indoor pedestrian positioning accuracy despite magnetic disturbances.

Keywords:

convolutional neural network indoor positioning magnetic interference pedestrian dead reckoning support vector machine unscented Kalman filter

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Area of Science:

Sensor technology and data analysis
Human motion analysis
Robotics and navigation

Background:

Micro-electro-mechanical systems (MEMS) sensors are increasingly integrated into smart devices for human motion analysis.
Magnetic interference in smartphone sensors degrades heading estimation accuracy, limiting applications.

Purpose of the Study:

To develop a robust method for detecting magnetic interference using sensor data.
To improve indoor pedestrian positioning accuracy in the presence of magnetic disturbances.

Main Methods:

A convolutional neural network (CNN) was used for automatic feature extraction from pedestrian motion data.
A support vector machine (SVM) was employed for interference detection based on extracted features.
Heading estimation strategies were developed for both interference-free and interfered environments.

Main Results:

The proposed method achieved a 99.38% accuracy in detecting magnetic interference.
Evaluations showed low average heading absolute errors (2.1891° and 1.5805°) and positioning errors (0.7565 m and 0.3856 m) on different trajectories.
The approach demonstrated robustness in enhancing indoor pedestrian positioning accuracy.

Conclusions:

The synergistic CNN-SVM approach effectively detects magnetic interference in sensor data.
The method significantly enhances the accuracy of indoor pedestrian positioning systems.
This research offers a viable solution for reliable navigation in challenging magnetic environments.