A Multi-Model Fusion-Based Indoor Positioning System Using Smartphone Inertial Measurement Unit Sensor Data
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
We propose novel multi-model fusion-based step detection and step length estimation
approaches that use the Kalman filter. The proposed step detection approach combines results from three
conventional step detection algorithms, namely, findpeaks, localmax, and advanced zero crossing to obtain
a single and more accurate step count estimate. The proposed step length estimation approach combines
results from two popular step length estimation algorithms namely Weinberg’s and Kim’s methods.
In our experiment, we consider five different smartphone placements, that is, when the smartphone is
handheld, handheld with an arm swing, placed in the backpack, placed in a trousers’ back pocket and
placed in a handbag. The system relies on inertia measurement unit sensors embedded in smartphones to
generate accelerometer, gyroscope and magnetometer values from the human subject’s motion. Results
from our experiments show that our proposed fusion based step detection and step length estimation
approaches outperform the convectional step detection and step length estimation algorithms, respectively.
Our Kalman fusion approach achieves a better step detection, step length estimation for all the five smart
phone placements hence providing a better positioning accuracy. The performance of the proposed multimodel
fusion-based positioning system was measured using the root mean square error (RMSE) of the
displacement errors and step count errors exhibited by all the the step length and step count algorithms. The
results show that the proposed Kalman fusion approach for step count estimation and step length estimation
provides the least RMSE for all the smartphone placements. The proposed approach provides an average
RMSE of 0.26 m in terms of the final position estimate for all the smartphone placements.
Description
Keywords
Indoor positioning, Kalman filter, Multi-model Fusion, Pedestrian dead reckoning, Smartphone IMU sensors
Citation
Priscilla, A., Steven Eyobu, O., Oyana, T. J., & Seog Han, D. (2020). A Multi-Model Fusion-Based Indoor Positioning System Using Smartphone Inertial Measurement Unit Sensor Data. IEEE
URI
https://scholar.archive.org/work/7k5vbjv2vnbqbayoxar64ya4fu/access/wayback/https://s3-eu-west-1.amazonaws.com/pstorage-techrxiv-6044451694/25368176/A_Multi_Model_Fusion_Based_Indoor_Positioning_System_Using_Smartphone_Inertial_Measurement_Unit_Sensor_Data_PREPRINT20201103.pdf
https://nru.uncst.go.ug/handle/123456789/7511
https://nru.uncst.go.ug/handle/123456789/7511