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

Haval Darwesh Abdalkarim; Halgurd Sarhang Maghdid

doi:10.24017/science.2019.2.5

Authors

Haval Darwesh Abdalkarim Network Department, Computer Science Institute, Sulaimani Polytechnique University, Sulaimani, Iraq
Halgurd Sarhang Maghdid Department of Software Engineering, Faculty of Engineering, Koya University, Koya, Erbil., Iraq https://orcid.org/0000-0003-1109-4009

Abstract

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.

Keywords:

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

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.

References

[1] C. Langlois, S. Tiku and S. Pasricha, "Indoor Localization with Smartphones: Harnessing the Sensor Suite in Your Pocket," in IEEE Consumer Electronics Magazine, vol. 6, no. 4, pp. 70-80, Oct. 2017.
https://doi.org/10.1109/MCE.2017.2714719
[2] A. Correa, M. Barcelo, A. Morell and J. L. Vicario, "A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications," Sensors, vol. 17, no. 8, p. 1927, Aug 2017.
https://doi.org/10.3390/s17081927
[3] S. A. Maghdid, H. S. Maghdid, S. R. HmaSalah, K. Z. Ghafoor, A. S. Sadiq and S. Khan, "Indoor human tracking mechanism using integrated onboard smartphones Wi-Fi device and inertial sensors," Telecommunication Systems, pp. 1-12, 2018.
https://doi.org/10.1109/ARESX.2018.8723267
[4] J. Kuang, X. Niu and X. Chen, "Robust Pedestrian Dead Reckoning Based on MEMS-IMU for Smartphones," Sensors, vol. 18, no. 5, p. 1391, May 2018.
https://doi.org/10.3390/s18051391
[5] Z.-A. Deng, G. Wang, D. Qin, Z. Na, Y. Cui and J. Chen, "Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks," Sensors, vol. 16, no. 9, p. 1427, Sep 2016.
https://doi.org/10.3390/s16091427
[6] V.-C. Ta, "Smartphone-based indoor positioning using Wi-Fi, inertial sensors and Bluetooth" M. S. thesis, School of Mathematics, Sciences and Technologies of Information, Computer Science, University Grenoble Alpes, Saint-Martin-d'Hères, France, 2017. Accessed on: Sep, 12, 2019. Available: https://tel.archives-ouvertes.fr/tel-01883828.
[7] J. Ying, K. Pahlavan and L. Xu, "Using Smartphone Sensors for Localization in BAN, Medical Internet of Things (m-IoT) - Enabling Technologies and Emerging Applications," 28 5 2019. [Online]. Available: https://www.intechopen.com/books/medical-internet-of-things-m-iot-enabling-technologies-and-emerging-applications/using-smartphone-sensors-for-localization-in-ban.
https://doi.org/10.5772/intechopen.80472
[8] W. Sakperea, M. Adeyeye-Oshinb and N. B. Mlitwac, "A state-of-the-art survey of indoor positioning and navigation systems and technologies," South African Computer Journal vol 29, no. 3, pp. 145-197, 2017.
https://doi.org/10.18489/sacj.v29i3.452
[9] L. Pei, D. Liu, D. Zou, L. F. C. Ronald, Y. Chen and Z. He, "Optimal Heading Estimation Based Multidimensional Particle Filter for Pedestrian Indoor Positioning," IEEE Access, vol. 6, pp. 49705-49720, 2018.
https://doi.org/10.1109/ACCESS.2018.2868792
[10] J. Kuang, X. Niu, P. Zhang and X. Chen, "Indoor Positioning Based on Pedestrian Dead Reckoning and Magnetic Field Matching for Smartphones," Sensors, vol. 18, no. 12, p. 4142, Nov 2018.
https://doi.org/10.3390/s18124142
[11] Z. Zhou, T. Chen and L. Xu, "An Improved Dead Reckoning Algorithm for Indoor Positioning Based on Inertial Sensors," in Proc. International Conference of Electrical, Automation and Mechanical Engineering (EAME 2015), 2015, pp. 369-371.
[12] Y. Liu, M. Dashti, M. A. A. ABd Rahman and J. Zhang, "Indoor Localization using Smartphone Inertial Sensors," in Workshop on Positioning, Navigation and Communication (WPNC), Dresden, Germany, 2014.
https://doi.org/10.1109/WPNC.2014.6843288
[13] A. Solin, S. Cortes, E. Rahtu and J. Kannala, "Inertial Odometry on Handheld Smartphones," in Proc. International Conference on Information Fusion, FUSION 2018, 2018, pp. 1361-1368.
https://doi.org/10.23919/ICIF.2018.8455482
[14] V. Marotto, A. Serra, D. Carbouni, M. Sole, T. Dessì and A. Manchinu, "Orientation Analysis through a Gyroscope Sensor for Indoor Navigation Systems," in Proc. The Fourth International Conference on Sensor Device Technologies and Applications, 2013, pp. 85-90.
[15] R. Zhang, A. Bannoura, F. Hoflinger, L. M. Reindl and C. Schindelhauer, "Indoor Localization Using A Smart Phone," 2013 IEEE Sensors Applications Symposium, SAS 2013 - Proceedings, pp. 38-42, Sep 2017.
https://doi.org/10.1109/SAS.2013.6493553