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access icon free Local HMM for indoor positioning based on fingerprinting and displacement ranging

© The Institution of Engineering and Technology
Received 15/09/2017, Accepted 22/02/2018, Revised 22/12/2017, Published 08/03/2018

Received signal strength (RSS) in wireless networks is widely adopted for indoor positioning purpose because of its low cost and open access properties. However due to the sophisticated propagation of radio signals, the RSS shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, the authors present a novel method to improve the indoor pedestrian positioning accuracy by modelling fingerprinting and information on the movement into a hidden Markov models (HMMs). They divide the whole continuous positioning process into specified-size sub-processes, which could efficiently reduce the accumulative and resonance error caused by iterative estimation. They use an accelerometer sensor to provide the information on the movement distance to calculate the transition probability of the HMMs. In their experiments, they demonstrate that, compared with the deterministic pattern matching algorithm, the proposed method greatly improves the positioning accuracy and shows robust environmental adaptability.

Inspec keywords: indoor navigation; pedestrians; indoor communication; pattern matching; accelerometers; indoor radio; hidden Markov models; radionavigation

Other keywords: indoor pedestrian positioning accuracy; hidden Markov models; movement distance; pedestrian walking; radio signals; wireless networks; open access properties; local HMM; critical errors; deterministic pattern matching algorithm; low cost; continuous positioning process; modelling fingerprinting; received signal strength; displacement; accumulative resonance error; deterministic indoor positioning; indoor positioning purpose; significant variation; RSS; HMMs; sophisticated propagation

Subjects: Radio links and equipment; Other topics in statistics; Markov processes

References

    1. 1)
      • 10. Ni, Y., Liu, J., Liu, S., et al: ‘An indoor pedestrian positioning method using HMM with a fuzzy pattern recognition algorithm in a WLAN fingerprint system’, Sensors, 2016, 16, (9), pp. 1447.
    2. 2)
      • 20. Forney, G.D: ‘The Viterbi algorithm’, Proc. IEEE, 1973, 61, (5), pp. 268–278.
    3. 3)
      • 2. Wang, J., Ma, Y., Zhao, Y., et al: ‘A multipath mitigation localization algorithm based on MDS for passive UHF RFID’, IEEE Commun. Lett., 2015, 19, (9), pp. 1652–1655.
    4. 4)
      • 19. Ayodele, T.O: ‘Introduction to machine learning’ (MIT Press, Cambridge, MA, USA, 2004), pp. 104–138.
    5. 5)
      • 6. Kaemarungsil, K., Krishnamurthy, P: ‘Modeling of indoor positioning systems based on location fingerprinting’, IEEE INFOCOM, 2004, 2, (2), pp. 1012–1022.
    6. 6)
      • 17. Wallbaum, M., Spaniol, O.: ‘Indoor positioning usingwireless local area networks’. IEEE John Vincent Atanasoff 2006 Int. Symp. on Modern Computing IEEE Computer Society, Sofia, Bulgaria, October 2006, pp. 17–26.
    7. 7)
      • 7. Jiang, Q., Ma, Y., Liu, K., et al: ‘A probabilistic radio map construction scheme for crowdsourcing-based fingerprinting localization’, IEEE Sens. J., 2016, 16, (10), pp. 3764–3774.
    8. 8)
      • 5. Yin, J., Yang, Q., Ni, L.M.: ‘Learning adaptive temporal radio maps for signal strength-based location estimation’, IEEE Trans. Mob. Comput., 2008, 7, (7), pp. 869–883.
    9. 9)
      • 11. Seitz, J., Vaupel, T., Meyer, S., et al: ‘A hidden Markov model for pedestrian navigation’. Positioning Navigation & Communication, Dresden, Germany, 2010, pp. 120–127.
    10. 10)
      • 1. Vaughan-Nichols, S.J.: ‘Will mobile computing's future be location, location, location?’, Computer, 2009, 42, (2), pp. 14–17.
    11. 11)
      • 13. Bahl, P., Padmanabhan, V.N., Balachandran, A.: ‘Enhancements to the RADAR user location and tracking system’, Microsoft Research, February, 2000.
    12. 12)
      • 18. Rabiner, L., Juang, B.H.: ‘Fundamentals of speech recognition’ (Prentice-Hall, Englewoord Cliffs, NJ, USA, 1993), Pearson Education, First Indian Reprint.
    13. 13)
      • 4. Niu, J., Wang, B., Shu, L., et al: ‘ZIL: an energy-efficient indoor localization system using ZigBee radio to detect WiFi fingerprints’, IEEE J. Sel. Areas Commun., 2015, 33, (7), pp. 1431–1442.
    14. 14)
      • 15. Hoang, M.K., Schmitz, S., Drueke, C., et al: ‘Server based indoor navigation using RSSI and inertial sensor information’, Positioning Navig. Commun., 2013, 13, (1), pp. 1–6.
    15. 15)
      • 21. Jasika, N., Alispahic, N., Elma, A., et al: ‘Dijkstra's shortest path algorithm serial and parallel execution performance analysis’. MIPRO, 2012 Proc. of the 35th Int. Convention, Opatija, Croatia, May 2012, pp. 1811–1815.
    16. 16)
      • 22. Honkavirta, V., Perälä, T., Alilöytty, S., et al: ‘A comparative survey of WLAN location fingerprinting methods’. Workshop on Positioning, Navigation and Communication, Hannover, Germany, March 2009, pp. 243–251.
    17. 17)
      • 14. Chen, L.H., Wu, H.K., Jin, M.H., et al: ‘Intelligent fusion of Wi-Fi and inertial sensor-based positioning systems for indoor pedestrian navigation’, IEEE Sens. J., 2014, 14, (11), pp. 4034–4042.
    18. 18)
      • 3. Gu, F., Niu, J., Duan, L.: ‘WAIPO: a fusion-based collaborative indoor localization system on smartphones’, IEEE/ACM Trans. Netw., 2017, 25, (4), pp. 2267–2280.
    19. 19)
      • 16. Liu, J., Chen, R., Pei, L., et al: ‘Accelerometer assisted robust wireless signal positioning based on a hidden Markov model’. IEEE/ION Position, Location and Navigation Symp., PLANS 2010, Indian Wells, CA, USA, May 2010, pp. 488–497.
    20. 20)
      • 12. Ma, Y., Dou, Z., Jiang, Q., et al: ‘An optimized HMM-based localization system using backward sequences matching algorithm exploiting geomagnetic information’, IEEE Sens. J., 2016, 16, (20), pp. 7472–7482.
    21. 21)
      • 23. Niu, J., Wang, B., Cheng, L., et al: ‘Wicloc: an indoor localization system based on WiFi fingerprints and crowdsourcing’. IEEE Int. Conf. on Communications, ICC 2015, London, United kingdom, September 2015, pp. 3008–3013.
    22. 22)
      • 9. Rabiner, L.R: ‘A tutorial on hidden Markov models and selected applications in speech recognition’, Read. Speech Recognit., 1990, 77, (2), pp. 267–296.
    23. 23)
      • 8. Kang, W., Han, Y.: ‘SmartPDR: smartphone-based pedestrian dead reckoning for indoor localization’, IEEE Sens. J., 2015, 15, (5), pp. 2906–2916.
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