MSPos: A Geomagnetic Matching Suitability-Incorporated Positioning Method in Indoor Environments

Numerous advanced geomagnetic positioning methods have made remarkable progress in indoor positioning. Nevertheless, the crucial factor of geomagnetic suitability, which significantly impacts positioning accuracy, has not been adequately considered. To address this problem, we propose a novel method named matching suitability-incorporated positioning (MSPos), which consists of two stages. In the geomagnetic matching suitability computational stage, we propose a connected features-based geomagnetic matching suitability computational method. We first extract geomagnetic connected features, then computing the evaluation values for each subregion based on the extracted features, and converting these values into the matching suitability. In the positioning stage, we propose a geomagnetic matching suitability-incorporated resilient positioning strategy. When the matching suitability of the subregion is strong, we propose a local spatial constraints-based positioning method to suppress the geomagnetic gross error at rare points. Conversely, if the subregion has weak matching suitability, we propose a pedestrian dead reckoning (PDR) heading assistance-based fusion method to reduce the error caused by geomagnetic mismatching. The resilient positioning strategy dynamically selects different methods incorporating the matching suitability of the subregion, where the pedestrian is situated. The performance of the method was evaluated in a real indoor environment. The experimental results show that the proposed MSPos method can achieve accurate and continuous positioning.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3548213
Citations
5
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
24.97

Funders

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article

MSPos: A Geomagnetic Matching Suitability-Incorporated Positioning Method in Indoor Environments

Ao Liu, Wenguang Wang, Yiming Li
5 citations
IEEE Transactions on Instrumentation and Measurement
Robotics and Sensor-Based Localization
24.97
article

MSPos: A Geomagnetic Matching Suitability-Incorporated Positioning Method in Indoor Environments

Ao Liu, Wenguang Wang, Yiming Li
article en
5 citations

Abstract

Numerous advanced geomagnetic positioning methods have made remarkable progress in indoor positioning. Nevertheless, the crucial factor of geomagnetic suitability, which significantly impacts positioning accuracy, has not been adequately considered. To address this problem, we propose a novel method named matching suitability-incorporated positioning (MSPos), which consists of two stages. In the geomagnetic matching suitability computational stage, we propose a connected features-based geomagnetic matching suitability computational method. We first extract geomagnetic connected features, then computing the evaluation values for each subregion based on the extracted features, and converting these values into the matching suitability. In the positioning stage, we propose a geomagnetic matching suitability-incorporated resilient positioning strategy. When the matching suitability of the subregion is strong, we propose a local spatial constraints-based positioning method to suppress the geomagnetic gross error at rare points. Conversely, if the subregion has weak matching suitability, we propose a pedestrian dead reckoning (PDR) heading assistance-based fusion method to reduce the error caused by geomagnetic mismatching. The resilient positioning strategy dynamically selects different methods incorporating the matching suitability of the subregion, where the pedestrian is situated. The performance of the method was evaluated in a real indoor environment. The experimental results show that the proposed MSPos method can achieve accurate and continuous positioning.

IEEE Transactions on Instrumentation and MeasurementVol. 74
China Academy of Information and Communications Technology (CN), Beihang University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 1%
Robotics and Sensor-Based Localization
24.97
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