A Multi-Model Fusion-Based Indoor Positioning System Using Smartphone Inertial Measurement Unit Sensor Data

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dc.contributor.authorAdong, Priscilla
dc.contributor.authorEyobu, Odongo Steven
dc.contributor.authorOyana, Tonny J.
dc.contributor.authorHan, Dong Seog
dc.date.accessioned2023-02-03T15:41:14Z
dc.date.available2023-02-03T15:41:14Z
dc.date.issued2020
dc.description.abstractWe 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.en_US
dc.identifier.citationPriscilla, 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. IEEEen_US
dc.identifier.urihttps://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
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7511
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectIndoor positioningen_US
dc.subjectKalman filteren_US
dc.subjectMulti-model Fusionen_US
dc.subjectPedestrian dead reckoningen_US
dc.subjectSmartphone IMU sensorsen_US
dc.titleA Multi-Model Fusion-Based Indoor Positioning System Using Smartphone Inertial Measurement Unit Sensor Dataen_US
dc.typeArticleen_US
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