Hybrid Onboard Smartphone Sensors Measurements to Improve Heading Estimation for Indoors Positioning Solutions

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Haval Darwesh Abdalkarim halgurd sarhang maghdid https://orcid.org/0000-0003-1109-4009


In the last decade, there is a significant progression and huge demand in using technology; specifically, those technologies are embedded in smartphones (SP). Examples of these technologies are embedding various sensors for multi-purposes. Positioning sensors (Accelerometer, Gyroscope, and Magnetometer) are one of the significant technologies. Besides this, indoor positioning services on smartphones are the main advantage of these sensors. There are many indoor positioning applications, for instance; billing, shopping, security and safety, indoor navigation, entertainment applications, and other point-of-interest (POI) applications. Nevertheless, precise position information through current positioning techniques is the main issue of these applications. The pedestrian dead reckoning (PDR) technique is one of the techniques in which the integration of onboard sensors is used for locating smartphones. Estimated distance, heading, and typical speed can be measured to determine the estimated position of the smartphone via using the PDR technique. The PDR technique offers a low positioning accuracy due to existing accumulated errors of the embedded sensors. To solve this issue, this article proposes a hybrid multi-sensors measurement to reduce the existing sensors drifts and errors and to increase estimated heading accuracy of the smartphone. Further, the sensors’ measurements with the previously estimated position are fused by using KALMAN Filter to determine the current location of the smartphone in each step of walking with better angular displacement accuracy. Proposed algorithm depends on increasing estimated angular displacement of the smartphone using combination of the integrated sensors’ measurements. The achieved positioning accuracy through the proposed approach and based on trial experiments is around 2 meters, which is equivalent to 10% improvement in comparison with state of the art.


localization; sensors; heading estimation; fusing multi-sensor.


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Author Biography

halgurd sarhang maghdid, Department of Software Engineering, Faculty of Engineering, Koya University, Koya, Erbil., Iraq

I received my BSc degree in Software Engineering from Salahaddin University, Erbil-Iraq (2004). And, I received my MSc degree in Computer Science from the Koya University in 2006, Koya- Erbil- Iraq, where continue as an instructor till now.  Recently (from 2016) I got the PhD  in Applied Computing at the University of Buckingham, UK. My research focuses on hybrid GNSS with other wireless/sensor technologies including WiFi, Bluetooth, and inertial sensors to offer seamless outdoors-indoors Smartphone localization.


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