[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A mobile localization algorithm based on fuzzy estimation for serious NLOS scenes

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

The indoor environment is intricate and the global positioning system (GPS) unable to satisfy the demand of indoor location accuracy. Therefore, the localization method based on wireless sensor network (WSN) has attached great importance and researched lately. The toughest issue to solve is the non-line of sight (NLOS) error caused by the uncertainty of the propagation environment. Hence, a location method based on hypothesis test and modified fuzzy probabilistic data association filter (HT-MFDAF) is proposed in this paper. Line-of-sight (LOS) and NLOS situations are regarded as an interactive Markov process. In the case of NLOS, we firstly identify and mitigate NLOS based on hypothesis testing theory. Then the ones which still have serious NOLS pollution is discarded by calculating similarity. Finally, the fuzzy membership degree is calculated by MFDAF, reconstructing the correlation probability to get the position estimate. The eventual location result is acquired by the Interactive Multiple Model (IMM) which weighted LOS and NLOS estimated position. Simulation and experimental results demonstrate the effectiveness of the algorithm.

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
£29.99 /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 includes VAT (United Kingdom)

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

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this article.

References

  1. Verma S, Zeadally S, Kaur S, Sharma AK (2021) Intelligent and secure clustering in wireless sensor network (WSN)-based intelligent transportation systems. IEEE Trans Intell Transport Syst

  2. Anvaripour M, Saif M, Ahmadi M (2020) A novel approach to reliable sensor selection and target tracking in sensor networks. IEEE Trans Industr Inf 16(1):171–182

    Article  Google Scholar 

  3. Chanak P, Banerjee I (2020) Congestion free routing mechanism for IoT-enabled wireless sensor networks for smart healthcare applications. IEEE Trans Consum Electron 66(3):223–232

    Article  Google Scholar 

  4. Naeem AR, Javed M, Rizwan S, Abbas JC, Lin W, Gadekallu TR (2021) DARE-SEP: a hybrid approach of distance aware residual energy-efficient SEP for WSN. IEEE Trans Green Commun Netw 5(2):611–621

    Article  Google Scholar 

  5. Yassin A et al (2017) Recent advances in indoor localization: a survey on theoretical approaches and applications. IEEE Commun Surv Tutor 19(2):1327–1346

  6. Park H, Chang J-H (2020) Robust localization based on ML-type, multi-stage ML-type, and extrapolated single propagation UKF methods under mixed LOS/NLOS conditions. IEEE Trans Wireless Commun 19(9):5819–5832

    Article  Google Scholar 

  7. LiangR et al (2020) Fault location method in power network by applying accurate information of arrival time differences of modal traveling waves. IEEE Trans Ind Inform 16(5):3124–3132

  8. Chen L, Ahriz I, Ruyet DL (2020) AoA-aware probabilistic indoor location fingerprinting using channel state information. IEEE Internet Things J 7(11):10868–10883

    Article  Google Scholar 

  9. Zhou M, Li Y, Tahir MJ, Geng X, Wang Y, He W (2021) Integrated statistical test of signal distributions and access point contributions for wi-fi indoor localization. IEEE Trans Veh Technol 70(5):5057–5070

    Article  Google Scholar 

  10. Cui W, Li B, Zhang L, Meng W (2019) Robust mobile location estimation in nlos environment using GMM, IMM, and EKF. IEEE Syst J 13(3):3490–3500

    Article  Google Scholar 

  11. Wu S, Zhang S, Xu K, Huang D (2019) Probability weighting localization algorithm based on NLOS identification in wireless network. Wirel Commun Mob Comput

  12. Aghaie N, Tinati MA (2016) Localization of WSN nodes based on NLOS identification using AOAs statistical information. In: 2016 24th Iranian Conference on Electrical Engineering (ICEE), pp 496–501

  13. Deng Z, Zheng X, Zhang C, Wang H, Yin L, Liu W (2020) A TDOA and PDR fusion method for 5G indoor localization based on virtual base stations in unknown areas. IEEE Access 8:225123–225133

    Article  Google Scholar 

  14. Sun R, Wang G, Fan Z, Xu T, Ochieng WY (2020) An integrated urban positioning algorithm using matching, particle swam optimized adaptive neuro fuzzy inference system and a spatial city model. IEEE Trans Veh Technol 69(5):4842–4854

    Article  Google Scholar 

  15. Cheng L, Hang J, Wang Y, Bi Y (2019) A fuzzy C-means and hierarchical voting based rssi quantify localization method for wireless sensor network. IEEE Access 7:47411–47422

    Article  Google Scholar 

  16. Wang Y, Gu K, Wu Y, Dai W, Shen Y (2020) NLOS effect mitigation via spatial geometry exploitation in cooperative localization. IEEE Trans Wireless Commun 19(9):6037–6049

    Article  Google Scholar 

  17. Silva B, Hancke GP (2020) Ranging error mitigation for through-the-wall non-line-of-sight conditions. IEEE Trans Ind Inform 16(11):6903–6911

  18. Liao J, Chen B (2006) Robust mobile location estimator with NLOS mitigation using interacting multiple model algorithm. IEEE Trans Wireless Commun 5(11):3002–3006

    Article  Google Scholar 

  19. Zhang Y, Chen HS, Luo Y (2014) A novel infrared landmark indoor positioning method based on improved IMM-UKF. Mech Mater 511–512

  20. Sun Y, Yang S, Wang G, Chen H (2021) Robust RSS-based source localization with unknown model parameters in mixed LOS/NLOS environments. IEEE Trans Veh Technol 70(4):3926–3931

    Article  Google Scholar 

  21. Ho T (2013) Urban location estimation for mobile cellular networks: a fuzzy-tuned hybrid systems approach. IEEE Trans Wireless Commun 12(5):2389–2399

    Article  Google Scholar 

  22. Ho T (2015) Robust urban wireless localization: synergy between data fusion, modeling and intelligent estimation. IEEE Trans Wireless Commun 14(2):685–697

    Article  Google Scholar 

  23. Yang CY, Chen BS, Liao FK (2010) Mobile location estimation using fuzzy-based IMM and data fusion. IEEE Trans Mobile Comput 9(10):1424–1436

    Article  Google Scholar 

  24. Oussalah M, De Schutter J (2002) Hybrid fuzzy probabilistic data association filter and joint probabilistic data association filter. Inf Sci 142:195–226

    Article  MATH  Google Scholar 

  25. Liangqun Li, Hongbing Ji, Xinbo G (2006) Maximum entropy fuzzy clustering with application to real-time target tracking. Signal Process 86:3432–3447

    Article  MATH  Google Scholar 

  26. Satapathi GS, Srihari P (2018) Rough fuzzy joint probabilistic association for tracking multiple targets in the presence of ECM. Expert Syst Appl 106:132–140

    Article  Google Scholar 

  27. Kumar S, Tiwari SN, Hegde RM (2015) Sensor node tracking using semi-supervised Hidden Markov Models. Ad Hoc Netw 33:55–70

    Article  Google Scholar 

  28. Alfakih M, Keche M, Benoudnine H, Meche A (2020) Improved Gaussian mixture modeling for accurate Wi-Fi based indoor localization systems. Phys Commun 43

  29. Wylie MP, Holtzman J (1996) The non-line of sight problem in mobile location estimation. Proc IEEE Univ Personal Commun Conf 2:827–831

    Article  Google Scholar 

  30. Cheng L, Xue M, Liu Z, Wang Y (2019) A robust tracking algorithm based on a probability data association for a wireless sensor network. Appl Sci 10

  31. Bar-Shalom Y, Fortmann TE (1981) Tracking and data association. Academic Press, New York

    MATH  Google Scholar 

  32. Fitzgerald RJ (1990) Development of practical PDA logic for multitarget tracking by microprocessor. In: Bar-Shalom Y (ed) Multitarget-multisensory tracking: Advanced application. Artech House, Noor wood, MA, pp 1–23

    Google Scholar 

  33. Hammes U, Zoubir AM (2011) Robust MT tracking based on M-estimation and interacting multiple model algorithm. IEEE Trans Signal Process 59(7):3398–3409

    Article  MathSciNet  MATH  Google Scholar 

  34. Cheng L, Zhang H, Wei D, Zhou J (2022) An indoor tracking algorithm based on particle filter and nearest neighbor data fusion for wireless sensor networks. Remote Sens 14(22):5791

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grant No.62273083 and No.61803077; Natural Science Foundation of Hebei Province under Grant No. F2020501012.

Author information

Authors and Affiliations

  1. Department of Computer and Communication Engineering, Northeastern University, Qinhuangdao, 066004, Hebei Province, China

    Yan Wang, Yuxin Gong & Huikang Yang

Authors
  1. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuxin Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huikang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yan Wang and Yuxin Gong wrote the main manuscript text and Huikang Yang prepared figures 14–18. All authors reviewed the manuscript.

Corresponding author

Correspondence to Yan Wang.

Ethics declarations

Ethical approval and consent to participate

Not applicable.

Human and animal ethics

Not applicable.

Consent for publication

All authors agree to publish in Peer-to-Peer Networking and Applications.

Competing interests

The authors declare that there is no conflict of interests/competing interests regarding the publication of this paper.

Additional information

Publisher's Note

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

This article is part of the Topical Collection: 1- Track on Networking and Applications

Guest Editor: Vojislav B. Misic

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Gong, Y. & Yang, H. A mobile localization algorithm based on fuzzy estimation for serious NLOS scenes. Peer-to-Peer Netw. Appl. 16, 2271–2289 (2023). https://doi.org/10.1007/s12083-023-01524-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-023-01524-7

Keywords

Search

Navigation