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A novel mobile target localization algorithm via HMM-based channel sight condition identification

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Abstract

When a target moves in the field of interest (FOI), the availability of channel sight information is important since the appropriate use of such information can help to improve the localization accuracy. In this paper, Cramer-Rao Lower Bounds (CRLB) for a multi-sensor localization system under different channel sight conditions are computed analytically, which shows that significant degradation in accuracy can happen if channel sight conditions are misidentified. A novel received signal strength (RSS)-based localization algorithm is next proposed. In the algorithm, the FOI is assumed can be divided into distinct non-overlapped cells, and the cells experienced by the moving target are modeled as a hidden Markov model (HMM). Once the HMM matrices are obtained by off-line training, channel sight conditions can be identified by deploying a real time forward-only algorithm. These identified channel sight conditions are used to localize the target. To reduce the position estimation errors which result from the possible misidentified channel sight conditions, a relocalization algorithm is then used to review the identified channel sight conditions once abnormality in position estimate is detected. The simulation results show that our proposed localization strategy can provide good localization performance with the localization error approaching that when all channel sight conditions are known.

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References

  1. Zhang Y, He S, Chen J (2015) Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Trans Netw 24(3):1632–1646

    Article  Google Scholar 

  2. Zhang H, Cheng P, Shi L, Chen L (2016) Optimal DoS attack scheduling in wireless networked control system. IEEE Trans Control Syst Technol 24(3):843–852

    Article  Google Scholar 

  3. Chen J, Xu W, He S, Sun Y, Thulasiraman P, Shen X S (2010) Utility-based asynchronous flow control algorithm for wireless sensor networks. IEEE J Sel Areas Commun 28(7):1116–1126

    Article  Google Scholar 

  4. Chen J, Yu Q, Chai B, Sun Y, Fan Y, Shen X (2015) Dynamic channel assignment for wireless sensor networks: a regret matching based approach. IEEE Trans Parallel Distrib Syst 26(1):95– 106

    Article  Google Scholar 

  5. Meng W, Yang Q, Sun Y (2016) Guaranteed performance control of dfig variable-speed wind turbines. IEEE Trans Control Syst Technol. doi:10.1109/TCST.2016.2524531

  6. He J, Cheng P, Shi L, Chen J, Sun Y (2014) Time synchronization in wsns: a maximum-value-based consensus approach. IEEE Trans Autom Control 59(3):660–675

    Article  MathSciNet  Google Scholar 

  7. Mao G, Fidan B, Anderson B (2007) Wireless sensor network localization techniques. Comput Netw 51 (10):2529–2553

    Article  MATH  Google Scholar 

  8. Yu K, Guo YJ (2009) Statistical NLOS identification based on AOA, TOA, and signal strength. IEEE Trans Veh Technol 58(1):274–286

    Article  Google Scholar 

  9. Fu Y, Ling Q, Tian Z (2012) Distributed sensor allocation for multi-target tracking in wireless sensor networks. IEEE Trans Aerossp Electron Syst 48(4):3538–3553

    Article  Google Scholar 

  10. Chalise BK, Zhang YD, Amin MG, Himed B (2014) Target localization in a multi-static passive radar system through convex optimization. Signal Process 102:207–215

    Article  Google Scholar 

  11. Lin L, So H-C, Chan FKW, Chan YT, Ho KC (2013) A new constrained weighted least squares algorithm for TDOA-based localization. Signal Process 93(11):2872–2878

    Article  Google Scholar 

  12. Doǧançay K, Hmam H (2008) Optimal angular sensor separation for aoa localization. Signal Process 88 (5):1248–1260

    Article  MATH  Google Scholar 

  13. Khodjaev J, Park Y, Malik A S (2010) Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments. Ann Telecommun-Annales des Télécommunications 65(5–6):301–311

    Article  Google Scholar 

  14. Yu K, Dutkiewicz E (2013) NLOS identification and mitigation for mobile tracking. IEEE Trans Aerosp Electron Syst 49(3): 1438–1452

    Article  Google Scholar 

  15. Borras J, Hatrack P, Mandayam NB (1998) Decision theoretic framework for NLOS identification. In: 48th IEEE vehicular technology conference, vol 2, pp 1583–1587

  16. Venkatesh S, Buehrer R M (2007) Non-line-of-sight identification in ultra-wideband systems based on received signal statistics. IET Microwaves Antennas Propag 1(6):1120–1130

    Article  Google Scholar 

  17. Heidari M, Akgul F O, Pahlavan K (2007) Identification of the absence of direct path in indoor localization systems. In: IEEE 18th international symposium on personal, indoor and mobile radio communications, pp 1–6

  18. Guvenc I, Chong C-C, Watanabe F (2007) NLOS identification and mitigation for UWB localization systems. In: Wireless communications and networking conference, pp 1571–1576

  19. Chen P (1999) A non-line-of-sight error mitigation algorithm in location estimation. In: IEEE wireless communications and networking conference, pp 316–320

  20. Wu S, Ma Y, Zhang Q, Zhang N (2007) NLOS error mitigation for UWB ranging in dense multipath environments. In: IEEE wireless communications and networking conference , pp 1565–1570

  21. Wann C-D, Hsueh C-S (2007) NLOS mitigation with biased Kalman filters for range estimation in UWB systems. In: IEEE Region 10 conference TENCON, pp 1–4

  22. Morelli C, Nicoli M, Rampa V, Spagnolini U (2007) Hidden Markov models for radio localization in mixed LOS/NLOS conditions. IEEE Trans Signal Process 55(4):1525–1542

    Article  MathSciNet  Google Scholar 

  23. Moghtadaiee V, Dempster AG, Lim S (2011) Indoor localization using fm radio signals: a fingerprinting approach. In: 2011 international conference on indoor positioning and indoor navigation (IPIN). IEEE, pp 1–7

  24. Kaemarungsi K, Krishnamurthy P (2012) Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Pervasive Mob Comput 8(2):292–316

    Article  Google Scholar 

  25. Shi X, Chew Y H, Yuen C, Yang Z (2014) A RSS-EKF localization method using HMM-based LOS/NLOS channel identification. In: 2014 IEEE international conference on communications (ICC). IEEE, pp 160–165

  26. Qi Y, Kobayashi H, Suda H (2006) Analysis of wireless geolocation in a non-line-of-sight environment. IEEE Trans Wirel Commun 5(3):672–681

    Article  Google Scholar 

  27. Hara S, Anzai D, Yabu T, Lee K, Derham T, Zemek R (2013) A perturbation analysis on the performance of TOA and TDOA localization in mixed LOS/NLOS environments. IEEE Trans Commun 61 (2):679–689

    Article  Google Scholar 

  28. Volkov AS (2015) Accuracy bounds of non-gaussian bayesian tracking in a NLOS environment. Signal Process 108:498–508

    Article  Google Scholar 

  29. Yin F, Fritsche C, Gustafsson F, Zoubir A M (2013) Received signal strength-based joint parameter estimation algorithm for robust geolocation in LOS/NLOS environments. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 6471–6475

  30. 3rd Generation Partnership Project (3GPP) (2010) Further advancements for EUTRA physical layer aspects (Release 9). 3GPP TR 36.814, V9.0.0

  31. SCM Text V5.0. Spatial channel model text description

  32. Patwari N, Ash JN, Kyperountas S, Hero A O III, Moses R L, Correal Ns S (2005) Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Process Mag 22(4): 54–69

    Article  Google Scholar 

  33. Gustafsson F, Gunnarsson F (2005) Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements. IEEE Signal Process Mag 22(4):41–53

    Article  Google Scholar 

  34. Bishop AN, Fidan B, Anderson B, Doǧançay K, Pathirana PN (2010) Optimality analysis of sensor-target localization geometries. Automatica 46(3):479–492

    Article  MathSciNet  MATH  Google Scholar 

  35. Ucinski D (2000) Optimal sensor location for parameter estimation of distributed processes. Int J Control 73 (13):1235–1248

    Article  MathSciNet  MATH  Google Scholar 

  36. Yang Z, Shi X, Chen J (2014) Optimal coordination of mobile sensors for target tracking under additive and multiplicative noises. IEEE Trans Ind Electron 61(7):3459–3468

    Article  Google Scholar 

  37. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

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Acknowledgments

This work is partly supported by 973 Program under Grant 2013CB329503, NSFC under Grant 61371159, and SUTD Temasek Lab (TELAMON – IRPS).

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Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

    Xiufang Shi & Zaiyue Yang

  2. Institute for Infocomm Research, Singapore, Singapore

    Yong Huat Chew

  3. Singapore University of Technology and Design, Singapore, Singapore

    Chau Yuen

Authors
  1. Xiufang Shi
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  2. Yong Huat Chew
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  3. Chau Yuen
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  4. Zaiyue Yang
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Correspondence to Zaiyue Yang.

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Shi, X., Chew, Y.H., Yuen, C. et al. A novel mobile target localization algorithm via HMM-based channel sight condition identification. Peer-to-Peer Netw. Appl. 10, 808–822 (2017). https://doi.org/10.1007/s12083-016-0484-x

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  • DOI: https://doi.org/10.1007/s12083-016-0484-x

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