Abstract
The fingerprinting-based positioning has great potential for indoor location estimation where GPS signals are mostly blocked. However, fingerprinting-based methods need a calibration step for establishing a fingerprint map. The site survey process should be performed to record fingerprints which is a labor-intensive task essentially in large buildings. In this paper, we address the pedestrian trajectory reconstruction problem for fingerprint map creation. Where the goal is to predict and refine users’ trajectories obtained from smartphone sensor measurements. Our proposed spatial–temporal matching mechanism consists of three stages. First, the initial trajectory is calculated using the PDR algorithm. Then, landmarks error is eliminated using the proposed forward/backward error correction (FEC/BEC) algorithm. Afterward, the proposed dynamic time warping-based path-matching (DTW-PM) method applies to handle map-related errors. The evaluation results show positioning accuracy improves up to 53.69%. Finally, a traditional KNN algorithm is performed to evaluate the positioning efficiency over generated radio map, which validates the quality of the obtained radio map.
Similar content being viewed by others
References
Basiri, A.; et al.: Indoor location based services challenges, requirements and usability of current solutions. Comput. Sci. Rev. 24, 1–12 (2017). https://doi.org/10.1016/j.cosrev.2017.03.002
Uphaus, P.O.; Beringer, B.; Siemens, K.; Ehlers, A.; Rau, H.: Location-based services–the market: success factors and emerging trends from an exploratory approach. J. Locat. Based Serv. 15, 1–26 (2021). https://doi.org/10.1080/17489725.2020.1868587
Woo, S.; Jeong, S.; Mok, E.; Xia, L.; Choi, C.; Pyeon, M.; Heo, J.: Application of WiFi-based indoor positioning system for labor tracking at construction sites: a case study in Guangzhou MTR. Autom. Constr. 20, 3–13 (2011). https://doi.org/10.1016/j.autcon.2010.07.009
Santos, R.; Leonardo, R.; Barandas, M.; Moreira, D.; Rocha, T.; Alves, P.; Oliveira, J.P.; Gamboa, H.: Crowdsourcing-based fingerprinting for indoor location in multi-storey buildings. IEEE Access. 9, 31143–31160 (2021). https://doi.org/10.1109/ACCESS.2021.3060123
Alitaleshi, A.; Jazayeriy, H.; Kazemitabar, J.: Affinity propagation clustering-aided two-label hierarchical extreme learning machine for Wi-Fi fingerprinting-based indoor positioning. J. Ambient Intell. Humaniz. Comput. 2022, 1–15 (2022). https://doi.org/10.1007/S12652-022-03777-1
Tiglao, N.M.; Alipio, M.; Dela Cruz, R.; Bokhari, F.; Rauf, S.; Khan, S.A.: Smartphone-based indoor localization techniques: state-of-the-art and classification. Measurement 179, 109349 (2021). https://doi.org/10.1016/j.measurement.2021.109349
Li, C.T.; Cheng, J.C.P.; Chen, K.: Top 10 technologies for indoor positioning on construction sites. Autom. Constr. 118, 103309 (2020). https://doi.org/10.1016/j.autcon.2020.103309
Ashraf, I.; Hur, S.; Park, Y.: Smartphone sensor based indoor positioning: current status, opportunities, and future challenges. Electron. (2020). https://doi.org/10.3390/electronics9060891
Otero, R.; Lagüela, S.; Garrido, I.; Arias, P.: Mobile indoor mapping technologies: a review. Autom. Constr. 120, 103399 (2020). https://doi.org/10.1016/j.autcon.2020.103399
Khalajmehrabadi, A.; Gatsis, N.; Akopian, D.: Modern WLAN fingerprinting indoor positioning methods and deployment challenges. IEEE Commun. Surv. Tutorials. 19, 1974–2002 (2017). https://doi.org/10.1109/COMST.2017.2671454
Zhu, X.; Qu, W.; Qiu, T.; Zhao, L.; Atiquzzaman, M.; Wu, D.O.: Indoor intelligent fingerprint-based localization: principles, approaches and challenges. IEEE Commun. Surv. Tutorials. 22, 2634-2657 (2020). https://doi.org/10.1109/comst.2020.3014304
Liu, H.H.: The quick radio fingerprint collection method for a WiFi-based indoor positioning system. Mob. Netw. Appl. 22, 61–71 (2017). https://doi.org/10.1007/s11036-015-0666-4
Liu, H.H.; Liu, C.: Implementation of wi-fi signal sampling on an android smartphone for indoor positioning systems. Sensors 18, 3 (2018). https://doi.org/10.3390/s18010003
Tan, J.; Fan, X.; Wang, S.; Ren, Y.: Optimization-based Wi-Fi radio map construction for indoor positioning using only smart phones. Sensors (Switzerland). (2018). https://doi.org/10.3390/s18093095
Gu, Y., Zhou, C., Wieser, A., Zhou, Z.: WiFi based trajectory alignment, calibration and crowdsourced site survey using smart phones and foot-mounted IMUs, 2017 Int. Conf. Indoor Position. Indoor Navig. IPIN 2017. 2017-Janua (2017) 1–6. https://doi.org/10.1109/IPIN.2017.8115929
Brida, P.; Machaj, J.; Racko, J.; Krejcar, O.: Algorithm for dynamic fingerprinting radio map creation using IMU measurements. Sensors 21(7), 2283 (2021). https://doi.org/10.3390/s21072283
Wang, X.; Wang, X.; Mao, S.; Zhang, J.; Periaswamy, S.C.G.; Patton, J.: Indoor radio map construction and localization with deep Gaussian processes. IEEE Internet Things J. 7, 11238–11249 (2020). https://doi.org/10.1109/JIOT.2020.2996564
Racko, J.; Machaj, J.; Brida, P.: Wi-Fi fingerprint radio map creation by using interpolation. Procedia Eng. (2017). https://doi.org/10.1016/j.proeng.2017.06.130
Wu, Y.; Zhu, H.B.; Du, Q.X.; Tang, S.M.: A survey of the research status of pedestrian dead reckoning systems based on inertial sensors. Int. J. Autom. Comput. 16, 65–83 (2019). https://doi.org/10.1007/s11633-018-1150-y
Harle, R.: A survey of indoor inertial positioning systems for pedestrians. IEEE Commun. Surv. Tutorials. 15, 1281–1293 (2013). https://doi.org/10.1109/SURV.2012.121912.00075
Antsfeld, L., Chidlovskii, B., Sansano-Sansano, E.: Deep smartphone sensors-WiFi fusion for indoor positioning and tracking (2020). http://arxiv.org/abs/2011.10799
Lima, W.S.; Souto, E.; El-Khatib, K.; Jalali, R.; Gama, J.: Human activity recognition using inertial sensors in a smartphone: an overview. Sensors (Switzerland). 19, 14–16 (2019). https://doi.org/10.3390/s19143213
Seo, J.; Laine, T.H.: Accurate position and orientation independent step counting algorithm for smartphones. J. Ambient Intell. Smart Environ. 10, 481–495 (2018). https://doi.org/10.3233/AIS-180503
Fan, Q.; Zhang, H.; Pan, P.; Zhuang, X.; Jia, J.; Zhang, P.; Zhao, Z.; Zhu, G.; Tang, Y.: Improved pedestrian dead reckoning based on a robust adaptive Kalman filter for indoor inertial location system. Sensors (Switzerland). (2019). https://doi.org/10.3390/s19020294
Yao, Y.; Pan, L.; Fen, W.; Xu, X.; Liang, X.; Xu, X.: A robust step detection and stride length estimation for pedestrian dead reckoning using a smartphone. IEEE Sens. J. 20, 9685–9697 (2020). https://doi.org/10.1109/JSEN.2020.2989865
Lee, J.H., Shin, B., Kim, C., Kim, J., Lee, S., Lee, T., Real time adaptive step length estimation for smartphone user, in: Int. Conf. Control. Autom. Syst., pp. 382–385. (2013) https://doi.org/10.1109/ICCAS.2013.6703929
Michel, T., Fourati, H., Geneves, P., Layaida, N.: A comparative analysis of attitude estimation for pedestrian navigation with smartphones, in: 2015 Int. Conf. Indoor Position. Indoor Navig. IPIN 2015, Institute of Electrical and Electronics Engineers Inc., (2015) https://doi.org/10.1109/IPIN.2015.7346767
Gu, F.; Hu, X.; Ramezani, M.; Acharya, D.; Khoshelham, K.; Valaee, S.; Shang, J.: Indoor localization improved by spatial context - a survey. ACM Comput. Surv. (2019). https://doi.org/10.1145/3322241
Nowicki, M.R.; Skrzypczyński, P.: A multi-user personal indoor localization system employing graph-based optimization. Sensors (Switzerland). 19, 157 (2019). https://doi.org/10.3390/s19010157
Ma, L., Fan, Y., Xu, Y., Cui, Y.: Pedestrian dead reckoning trajectory matching method for radio map crowdsourcing building in WiFi indoor positioning system, in: IEEE Int. Conf. Commun., Institute of Electrical and Electronics Engineers Inc., (2017) https://doi.org/10.1109/ICC.2017.7996457
Bang, Y.; Kim, J.; Yu, K.: An improved map-matching technique based on the fréchet distance approach for pedestrian navigation services. Sensors (Switzerland). (2016). https://doi.org/10.3390/s16101768
Zhu, J.; Cheng, D.; Zhang, W.; Song, C.; Chen, J.; Pei, T.: A new approach to measuring the similarity of indoor semantic trajectories. ISPRS Int. J. Geo-Information. 10, 90 (2021). https://doi.org/10.3390/ijgi10020090
Yu, C.; El-Sheimy, N.; Lan, H.; Liu, Z.: Map-based indoor pedestrian navigation using an auxiliary particle filter. Micromachines. 8, 1–16 (2017). https://doi.org/10.3390/mi8070225
Carrera Villacres, J.L.; Zhao, Z.; Braun, T.; Li, Z.: A particle filter-based reinforcement learning approach for reliable wireless indoor positioning. IEEE J. Sel. Areas Commun. 37, 2457–2473 (2019). https://doi.org/10.1109/JSAC.2019.2933886
Bataineh, S.; Bahillo, A.; Díez, L.; Onieva, E.; Bataineh, I.: Conditional random field-based offline map matching for indoor environments. Sensors. 16, 1302 (2016). https://doi.org/10.3390/s16081302
Xiao, Z., Wen, H., Markham, A., Trigoni, N.: Lightweight map matching for indoor localisation using conditional random fields, IPSN 2014 - Proc. 13th Int. Symp. Inf. Process. Sens. Networks (Part CPS Week). (2014) 131–142. https://doi.org/10.1109/IPSN.2014.6846747
Seo, J., Chiang, Y., Laine, T.H., Khan, A.M. Step counting on smartphones using advanced zero-crossing and linear regression, ACM IMCOM 2015 - Proc. (2015) https://doi.org/10.1145/2701126.2701223
Myo, W.W.; Wettayaprasit, W.; Aiyarak, P.: A more reliable step counter using built-in accelerometer in smartphone Indones. J. Electr. Eng. Comput. Sci. 12, 775–782 (2018). https://doi.org/10.11591/ijeecs.v12.i2.pp775-782
Weinberg, H.: Using the ADXL202 in pedometer and personal navigation applications. Analog Devices AN-602 Application Note, vol. 2. pp. 1–6 (2002)
Kok, M.; Hol, J.D.; Schön, T.B.: Using inertial sensors for position and orientation estimation. Found. Trends Signal Process. 11, 1–153 (2017). https://doi.org/10.1561/2000000094
Diebel, J.: Representing attitude: Euler Angles, unit quaternions, and rotation vectors, 2006. https://www.astro.rug.nl/software/kapteyn/_downloads/fa29752e4cd69adcfa2fc03b1c020f4e/attitude.pdf (accessed 16 July 2020)
Madgwick, S.O.H., Harrison, A.J.L, Vaidyanathan, R.: Estimation of IMU and MARG orientation using a gradient descent algorithm, in: IEEE Int. Conf. Rehabil. Robot., 2011. https://doi.org/10.1109/ICORR.2011.5975346
Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAI-94 Workshop on Knowledge Discovery in Databases, pp. 359–370. AAAI Press (1994)
Torres-Sospedra, J.; Jiménez, A.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.; Seco, F.; Pérez-Navarro, A.; Nicolau, M.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R.; Tsao, Y.: Off-line evaluation of mobile-centric indoor positioning systems: the experiences from the 2017 IPIN competition. Sensors. 18, 487 (2018). https://doi.org/10.3390/s18020487
Jimenez, A.R.; Seco, F.; Torres-Sospedra, J.: Tools for smartphone multi-sensor data registration and GT mapping for positioning applications, 2019 Int. Conf. Indoor Position. Indoor Navig. IPIN 2019, 1–9 (2019). https://doi.org/10.1109/IPIN.2019.8911784
Torres-Sospedra, J.; Moreira, A.: Analysis of sources of large positioning errors in deterministic fingerprinting. Sensors (Switzerland). 17, 1–48 (2017). https://doi.org/10.3390/s17122736
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Alitaleshi, A., Jazayeriy, H. & Kazemitabar, J. Indoor Pedestrian Trajectory Reconstruction Using Spatial–Temporal Error Correction and Dynamic Time Warping-Based Path Matching for Fingerprints Map Creation. Arab J Sci Eng 48, 2101–2119 (2023). https://doi.org/10.1007/s13369-022-07095-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-022-07095-8