Skip to main content

Advertisement

Springer Nature Link
Log in

An improvement in smartphone-based 3D indoor positioning using an effective map matching method

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Despite the existence of various sensor errors and users' complex movements, smartphone sensor-based positioning systems have significant potential for indoor location-based services (LBS). In this paper, a method for indoor positioning in three smartphone carrying modes (i.e., Texting, Calling, and Swinging) using accelerometer, gravity, magnetometer, gyroscope, and pressure sensors data is proposed. The data of the gyroscope, gravity, and magnetometer sensors are integrated to estimate the heading using the Kalman filter (KF) based on the quaternion of the orientation of the phone. To evaluate the heading estimation method, five different methods including the magnetic (combined with the accelerometer and gravity sensors data), Madgwick, Extended Kalman filter (EKF), and proposed KF are implemented independently for different carrying modes. Gravity and magnetometer information is used to update the gyroscope state model. Finally, the 2D position is estimated based on the length and heading information of the steps. To develop the system for 3D positioning applications, the pressure sensor of the smartphone is employed to measure the changes in the pressure related to the vertical displacement of the user and the changes at the floor level. However, this method as a relative positioning method suffers from the cumulative error problem, which prevents its independent implementation. The heading and step length errors cause deviation from the main path of movement and cut off the walls. A new map matching method is introduced by examining the user's position and the way the path crosses through the wall to prevent the wall-crossing problem. The results show that the proposed algorithm significantly improves the accuracy of position estimation. The mean absolute reference position error for the three smartphone carrying modes (i.e., texting, calling, and swinging) was reduced from 3.43 to 0.59, from 3.56 to 0.67, and from 4.83 to 0.77, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37
Fig. 38
Fig. 39
Fig. 40
Fig. 41
Fig. 42
Fig. 43
Fig. 44
Fig. 45
Fig. 46

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Android D (2019) Sensors overview. https://developer.android.com/guide/topics/sensors/sensors_overview

  • Benikovsky J, Brida P, Machaj J (2010) Localization in real GSM network with fingerprinting utilization. In: International conference on mobile lightweight wireless systems. Springer, pp 699–709

  • Chou JC (1992) Quaternion kinematic and dynamic differential equations. IEEE Trans Robot Autom 8:53–64

    Google Scholar 

  • Deng Z-A, Hu Y, Yu J, Na Z (2015a) Extended Kalman Filter for real time indoor localization by fusing WiFi and smartphone inertial sensors. Micromachines 6:523–543

    Google Scholar 

  • Deng Z-A, Wang G, Hu Y, Wu D (2015b) Heading estimation for indoor pedestrian navigation using a smartphone in the pocket. Sensors 15:21518–21536

    Google Scholar 

  • Deng Z-A, Wang G, Hu Y, Cui Y (2016) Carrying Position independent user heading estimation for indoor pedestrian navigation with smartphones. Sensors 16:677

    Google Scholar 

  • Diebel J (2006) Representing attitude: Euler angles, unit quaternions, and rotation vectors. Matrix 58:1–35

    Google Scholar 

  • Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271

    MathSciNet  MATH  Google Scholar 

  • Feng K, Li J, Zhang X, Shen C, Bi Y, Zheng T, Liu J (2017) A new quaternion-based Kalman filter for real-time attitude estimation using the two-step geometrically-intuitive correction algorithm. Sensors 17:2146

    Google Scholar 

  • Fourati H, Manamanni N, Afilal L, Handrich Y (2012) Complementary observer for body segments motion capturing by inertial and magnetic sensors. IEEE/ASME Trans Mechatron 19:149–157

    Google Scholar 

  • Górak R, Luckner M, Okulewicz M, Porter-Sobieraj J, Wawrzyniak P (2016) Indoor localisation based on GSM signals: multistorey building study mobile information systems 2016

  • Hazas M, Hopper A (2006) Broadband ultrasonic location systems for improved indoor positioning. IEEE Trans Mobile Comput 5:536–547

    Google Scholar 

  • Henderson DM (1977) Euler angles, quaternions, and transformation matrices for space shuttle analysis

  • Hoseinitabatabaei SA, Gluhak A, Tafazolli R, Headley W (2013) Design, realization, and evaluation of uDirect-An approach for pervasive observation of user facing direction on mobile phones. IEEE Trans Mobile Comput 13:1981–1994

    Google Scholar 

  • House S, Connell S, Milligan I, Austin D, Hayes TL, Chiang P (2011) Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials. In: 2011 annual international conference of the IEEE engineering in medicine and biology society, 2011. IEEE, pp 7598–7601

  • Hsu H-H, Chang J-K, Peng W-J, Shih TK, Pai T-W, Man KL (2018) Indoor localization and navigation using smartphone sensory data. Ann Oper Res 265:187–204

    Google Scholar 

  • Huang J, Yu X, Wang Y, Xiao X (2016) An integrated wireless wearable sensor system for posture recognition and indoor localization. Sensors 16:1825

    Google Scholar 

  • Husen MN, Lee S (2016) Indoor location sensing with invariant Wi-Fi received signal strength fingerprinting. Sensors 16:1898

    Google Scholar 

  • Ilkovičová Ľ, Kajánek P, Kopáčik A (2016) Pedestrian indoor positioning and tracking using smartphone sensors step detection and map matching algorithm. In: International symposium on engineering Geodesy, pp 20–22

  • Jacob R, Winstanley A, Togher N, Roche R, Mooney P (2012) Pedestrian navigation using the sense of touch. Comput Env Urban Syst 36:513–525

    Google Scholar 

  • Jiménez AR, Seco F (2016) Comparing Decawave and Bespoon UWB location systems: Indoor/outdoor performance analysis. In: 2016 international conference on indoor positioning and indoor navigation (IPIN), IEEE, pp 1–8

  • Jiménez AR, Seco F, Zampella F, Prieto JC, Guevara J (2011) PDR with a foot-mounted IMU and ramp detection. Sensors 11:9393–9410

    Google Scholar 

  • Kim SY, Yoon KS, Lee DH, Lee MH (2011) The localization of a mobile robot using a pseudolite ultrasonic system and a dead reckoning integrated system. Int J Control Autom Syst 9:339

    Google Scholar 

  • Kim Y, Shin H, Chon Y, Cha H (2013) Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem. Pervasive Mobile Comput 9:406–420

    Google Scholar 

  • Kunze K, Lukowicz P, Partridge K, Begole B (2009) Which way am I facing: Inferring horizontal device orientation from an accelerometer signal. In: 2009 international symposium on wearable computers, 2009. IEEE, pp 149–150

  • Lee G (2015) 3D coverage location modeling of Wi-Fi access point placement in indoor environment. Comput Env Urban Syst 54:326–335

    Google Scholar 

  • Lee K, Kwan M-P (2018) Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Comput Env Urban Syst 67:124–131

    Google Scholar 

  • Lee K, Lee J, Kwan M-P (2017) Location-based service using ontology-based semantic queries: a study with a focus on indoor activities in a university context. Comput Env Urban Syst 62:41–52

    Google Scholar 

  • Liu Y, Dashti M, Rahman MAA, Zhang J (2014) Indoor localization using smartphone inertial sensors. In: 2014 11th workshop on positioning, navigation and communication (WPNC), IEEE, pp 1–6

  • Llorca DF, Sotelo MA, Parra I, Ocaña M, Bergasa LM (2010) Error analysis in a stereo vision-based pedestrian detection sensor for collision avoidance applications. Sensors 10:3741–3758

    Google Scholar 

  • Madgwick SO, Harrison AJ, Vaidyanathan R (2011) Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE international conference on rehabilitation robotics. IEEE, pp 1–7

    Google Scholar 

  • Marins JL, Yun X, Bachmann ER, McGhee RB, Zyda MJ (2001) An extended Kalman filter for quaternion-based orientation estimation using MARG sensors. In: Proceedings 2001 IEEE/RSJ international conference on intelligent robots and systems. expanding the societal role of robotics in the the next millennium (Cat. No. 01CH37180), IEEE, pp 2003–2011

  • Martin E, Vinyals O, Friedland G, Bajcsy R (2010) Precise indoor localization using smart phones. In: Proceedings of the 18th ACM international conference on multimedia, pp 787–790

  • Moder T, Reitbauer C, Dorn M, Wieser M (2017) Calibration of smartphone sensor data usable for pedestrian dead reckoning. In: 2017 international conference on indoor positioning and indoor navigation (IPIN), IEEE, pp 1–8

  • Murata M, Ahmetovic D, Sato D, Takagi H, Kitani KM, Asakawa C (2019) Smartphone-based localization for blind navigation in building-scale indoor environments. Pervasive Mobile Comput 57:14–32

    Google Scholar 

  • Otsason V, Varshavsky A, LaMarca A, De Lara E (2005) Accurate GSM indoor localization. In: International conference on ubiquitous computing, 2005. Springer, pp 141–158

  • Parnian N, Golnaraghi F (2010) Integration of a multi-camera vision system and strapdown inertial navigation system (SDINS) with a modified Kalman filter. Sensors 10:5378–5394

    Google Scholar 

  • Paul AS, Wan EA (2008) Wi-Fi based indoor localization and tracking using sigma-point Kalman filtering methods. In: 2008 IEEE/ION position, location and navigation symposium, IEEE, pp 646–659

  • Poulose A, Eyobu OS, Han DS (2019) An indoor position-estimation algorithm using smartphone IMU sensor data. IEEE Access 7:11165–11177

    Google Scholar 

  • Qian J, Ma J, Ying R, Liu P, Pei L (2013) An improved indoor localization method using smartphone inertial sensors. In: International conference on indoor positioning and indoor navigation, IEEE, pp 1–7

  • Qiu C, Mutka MW (2018) Walk and learn: enabling accurate indoor positioning by profiling outdoor movement on smartphones. Pervasive Mobile Comput 48:84–100

    Google Scholar 

  • Ruiz ARJ, Granja FS, Honorato JCP, Rosas JIG (2011) Accurate pedestrian indoor navigation by tightly coupling foot-mounted IMU and RFID measurements. IEEE Trans Instrum Meas 61:178–189

    Google Scholar 

  • Saab SS, Nakad ZS (2010) A standalone RFID indoor positioning system using passive tags. IEEE Trans Industr Electron 58:1961–1970

    Google Scholar 

  • Saadatzadeh E, Chehreghan A, Ali Abbaspour R (2019) Pedestrian dead reckoning using smartphones sensors: an efficient indoor positioning system in complex buildings of smart cities. Int Arch Photogram Rem Sens Spat Inf Sci 2019:5

    Google Scholar 

  • Sabatini AM (2006) Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans Biomed Eng 53:1346–1356

    Google Scholar 

  • Sobers D, Yamaura S, Johnson E (2010) Laser-aided inertial navigation for self-contained autonomous indoor flight. In: AIAA guidance, navigation, and control conference, p 8211

  • Valenti RG, Dryanovski I, Xiao J (2015) A linear Kalman filter for MARG orientation estimation using the algebraic quaternion algorithm. IEEE Trans Instrum Meas 65:467–481

    Google Scholar 

  • Wang X, Jiang M, Guo Z, Hu N, Sun Z, Liu J (2016) An indoor positioning method for smartphones using landmarks and PDR. Sensors 16:2135

    Google Scholar 

  • Wang B, Liu X, Yu B, Jia R, Gan X (2018) Pedestrian dead reckoning based on motion mode recognition using a smartphone. Sensors 18:1811

    Google Scholar 

  • Weinberg H (2002) Using the ADXL202 in pedometer and personal navigation applications. In: Analog Devices AN-602 application note, vol 2, pp 1–6

  • Wu J, Zhou Z, Chen J, Fourati H, Li R (2016) Fast complementary filter for attitude estimation using low-cost MARG sensors. IEEE Sens J 16:6997–7007

    Google Scholar 

  • Wu D, Xia L, Geng J (2018) Heading estimation for pedestrian dead reckoning based on robust adaptive Kalman filtering. Sensors 18:1970

    Google Scholar 

  • Yang H, Zhang R, Bordoy J, Höflinger F, Li W, Schindelhauer C, Reindl L (2016) Smartphone-based indoor localization system using inertial sensor and acoustic transmitter/receiver. IEEE Sens J 16:8051–8061

    Google Scholar 

  • Zhang R, Bannoura A, Höflinger F, Reindl LM, Schindelhauer C (2013) Indoor localization using a smart phone. In: 2013 IEEE sensors applications symposium proceedings, 2013. IEEE, pp 38–42

  • Zhao H, Cheng W, Yang N, Qiu S, Wang Z, Wang J (2019a) Smartphone-based 3D indoor pedestrian positioning through multi-modal data fusion. Sensors 19:4554

    Google Scholar 

  • Zhao H, Zhang L, Qiu S, Wang Z, Yang N, Xu J (2019b) Pedestrian dead reckoning using pocket-worn smartphone. IEEE Access 7:91063–91073

    Google Scholar 

  • Zhou Y, Law CL, Guan YL, Chin F (2010) Indoor elliptical localization based on asynchronous UWB range measurement. IEEE Trans Instrum Meas 60:248–257

    Google Scholar 

  • Zhuang Y, Shen Z, Syed Z, Georgy J, Syed H, El-Sheimy N (2014) Autonomous WLAN heading and position for smartphones. In: 2014 IEEE/ION position, location and navigation symposium-PLANS 2014, IEEE, pp 1113–1121

  • Zhuang Y, Syed Z, Georgy J, El-Sheimy N (2015) Autonomous smartphone-based WiFi positioning system by using access points localization and crowdsourcing. Pervasive Mobile Comput 18:118–136

    Google Scholar 

  • Zhuang Y et al (2018) A survey of positioning systems using visible LED lights. IEEE Commun Surv Tutor 20:1963–1988

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Esmaiel Saadatzadeh & Rahim Ali Abbaspour

  2. Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

    Alireza Chehreghan

Authors
  1. Esmaiel Saadatzadeh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rahim Ali Abbaspour
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Alireza Chehreghan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Rahim Ali Abbaspour.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saadatzadeh, E., Ali Abbaspour, R. & Chehreghan, A. An improvement in smartphone-based 3D indoor positioning using an effective map matching method. J Ambient Intell Human Comput 14, 13741–13771 (2023). https://doi.org/10.1007/s12652-022-04027-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-022-04027-0

Keywords