A fusion based indoor positioning system using smartphone inertial measurement unit sensor data
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This study presents an Indoor Positioning Systems (IPS) based on Pedestrian Dead Reckoning (PDR) for localisation of pedestrians by indoor navigation applications. To improve the accuracy of the PDR algorithm, 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, find peaks, local max, and advanced zero-crossing to obtain a single and more accurate step count estimate while 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 trouser's back pocket and placed in a handbag. The system relies on inertia measurement unit sensors embedded in smartphones to generate mobility information. 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. We were able to achieve high step detection, step length estimation and positioning accuracy for all five smartphone placements. We obtained a RMSE of 0.6081, 0.6589, 0.7893, 0.7826 and 0.4480 for step detection, 0.0327, 0.0331, 0.1908, 0.2038 and 0.0359 meters for step length estimating and 0.2252, 0.1460, 0.3623, 0.4169 and 0.1509 meters for position estimation.