access icon free TOA NLOS mitigation cooperative localisation algorithm based on topological unit

© The Institution of Engineering and Technology
Received 11/01/2020, Accepted 25/11/2020, Revised 26/10/2020, Published 05/01/2021

The accuracy of cooperative localisation can be severely degraded in non-line-of-sight (NLOS) environments. To mitigate the NLOS errors, the cooperative localisation problem based on time of arrival (TOA) under the mixed line-of-sight (LOS)/NLOS conditions is addressed. By studying the topological relationship between nodes, a TOA NLOS mitigation cooperative localisation algorithm based on the topological unit is proposed. This algorithm is implemented under the classical multidimensional scaling framework. The adjacent topological unit of NLOS measurements are successfully identified by using the LOS matrix, and the NLOS measurements are re-estimated using topological units. The least-square method is used to transform the relative coordinates into absolute coordinates depending on the location of the anchor nodes. Compared to the existing methods, by employing the topological unit, this algorithm only requires the number of LOS anchor nodes to be 2 in the two-dimensional plane, and the better localisation performance can be achieved. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.

Inspec keywords: sensor placement; time-of-arrival estimation; least squares approximations; cooperative communication; wireless sensor networks; telecommunication network topology

Other keywords: NLOS errors; adjacent topological unit; time of arrival; LOS anchor nodes; TOA NLOS mitigation; nonline-of-sight environments; localisation performance; dense NLOS environments; LOS matrix; cooperative localisation problem; least-square method; sparse NLOS environments; NLOS measurements

Subjects: Communication network design, planning and routing; Other topics in statistics; Signal processing and detection; Interpolation and function approximation (numerical analysis); Wireless sensor networks

References

    1. 1)
      • 28. de Sousa, M.N., Thoma, R.S.: ‘Localization of UAV in urban scenario using multipath exploiting TDOA fingerprints’. 2018 IEEE 29th Annual Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), Politecnico di Milano, Milan, Italy, 2018, pp. 1394–1399.
    2. 2)
      • 45. Platt, J.C.: ‘Fastmap, metricmap, and landmark mds are all nystrom algorithms’. Proc. Int. Workshop on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2005, pp. 261–268.
    3. 3)
      • 25. Borras, J., Hatrack, P., Mandayam, N.B.: ‘Decision theoretic framework for NLOS identification’. VTC'98 48th IEEE Vehicular Technology Conf. Pathway to Global Wireless Revolution (Cat No 98CH36151), Ottawa, ON, Canada, 1998, vol. 2, pp. 1583–1587.
    4. 4)
      • 27. Venkatesh, S., Buehrer, R.: ‘Non-line-of-sight identification in ultra-wideband systems based on received signal statistics’, IET Microw. Antennas Propag., 2007, 1, (6), pp. 1120–1130.
    5. 5)
      • 3. Wang, G., Chen, H., Li, Y., et al: ‘NLOS error mitigation for TOA-based localization via convex relaxation’, IEEE Trans. Wirel. Commun., 2014, 13, (8), pp. 4119–4131.
    6. 6)
      • 32. Kapusuz, K.Y., Kara, A.: ‘Determination of scattering center of multipath signals using geometric optics and fresnel zone concepts’, Eng. Sci. Technol. Int. J., 2014, 17, (2), pp. 50–57.
    7. 7)
      • 20. Biswas, P., Lian, T.C., Wang, T.C., et al: ‘Semidefinite programming based algorithms for sensor network localization’, ACM Trans. Sensor Netw. (TOSN), 2006, 2, (2), pp. 188–220.
    8. 8)
      • 34. Liu, D., Wang, Y., He, P., et al: ‘TOA localization for multipath and nlos environment with virtual stations’, EURASIP J. Wirel. Commun. Netw., 2017, 2017, (1), pp. 1–7.
    9. 9)
      • 12. Chen, S., Zhang, J., Mao, Y., et al: ‘Efficient distributed method for NLOS cooperative localization in WSNs’, Sensors, 2019, 19, (5), p. 1173.
    10. 10)
      • 18. Guvenc, I., Chong, C.C., Watanabe, F.: ‘NLOS identification and mitigation for UWB localization systems’. 2007 IEEE Wireless Communications and Networking Conf., Hong Kong, 2007, pp. 1571–1576.
    11. 11)
      • 29. Haigh, S., Kulon, J., Partlow, A., et al: ‘A robust algorithm for classification and rejection of NLOS signals in narrowband ultrasonic localization systems’, IEEE Trans. Instrum. Meas., 2019, 68, (3), pp. 646–655.
    12. 12)
      • 37. Kuschel, H., Cristallini, D., Olsen, K.E.: ‘Tutorial: passive radar tutorial’, IEEE Aerosp. Electron. Syst. Mag., 2019, 34, (2), pp. 2–19.
    13. 13)
      • 4. O'connor, A., Setlur, P., Devroye, N.: ‘Single-sensor RF emitter localization based on multipath exploitation’, IEEE Trans. Aerosp. Electron. Syst., 2015, 51, (3), pp. 1635–1651.
    14. 14)
      • 23. Güvenç, I., Chong, C.C., Watanabe, F., et al: ‘NLOS identification and weighted least-squares localization for UWB systems using multipath channel statistics’, EURASIP J. Adv. Signal Process., 2007, 2008, pp. 1–14.
    15. 15)
      • 38. Hehn, M., Sippel, E., Carlowitz, C., et al: ‘High-accuracy localization and calibration for 5-dof indoor magnetic positioning systems’, IEEE Trans. Instrum. Meas., 2019, 68, (10), pp. 4135–4145.
    16. 16)
      • 26. Heidari, M., Akgul, F.O., Alsindi, N.A., et al: ‘Neural network assisted identification of the absence of direct path in indoor localization’. IEEE GLOBECOM 2007-IEEE Global Telecommunications Conf., Washington, DC, USA, 2007, pp. 387–392.
    17. 17)
      • 33. Dalveren, Y., Kara, A.: ‘Multipath exploitation in emitter localization for irregular terrains.’, Radioengineering, 2019, 28, (2), pp. 473–482.
    18. 18)
      • 46. Luo, X.l., Wu, Z.j.: ‘Least-squares approximations in geometric buildup for solving distance geometry problems’, J. Optim. Theory Appl., 2011, 149, (3), pp. 580–598.
    19. 19)
      • 43. Cheung, K.W., So, H.C.: ‘A multidimensional scaling framework for mobile location using time-of-arrival measurements’, IEEE Trans. Signal Process., 2005, 53, (2), pp. 460–470.
    20. 20)
      • 1. Cheung, K., Ma, W., So, H.C.: ‘Accurate approximation algorithm for TOA-based maximum likelihood mobile location using semidefinite programming’. IEEE Int. Conf. on Acoustics, Quebec, Canada, 2004, vol. 2, pp. 145–148.
    21. 21)
      • 24. Marano, S., Gifford, W.M., Wymeersch, H., et al: ‘NLOS identification and mitigation for localization based on UWB experimental data’, IEEE J. Sel. Areas Commun., 2010, 28, (7), pp. 1026–1035.
    22. 22)
      • 47. Venkatesh, S., Buehrer, R.M.: ‘NLOS mitigation using linear programming in ultrawideband location-aware networks’, IEEE Trans. Veh. Technol., 2007, 56, (5), pp. 3182–3198.
    23. 23)
      • 42. Cao, J., Wan, Q., Ouyang, X., et al: ‘Multidimensional scaling-based passive emitter localisation from time difference of arrival measurements with sensor position uncertainties’, IET Signal Process., 2016, 11, (1), pp. 43–50.
    24. 24)
      • 15. Venkatraman, S., Caffery, J., You, H.R.: ‘A novel TOA location algorithm using LOS range estimation for NLOS environments’, IEEE Trans. Veh. Technol., 2004, 53, (5), pp. 1515–1524.
    25. 25)
      • 6. Liu, Y., Guo, F., Yang, L., et al: ‘An improved algebraic solution for TDOA localization with sensor position errors’, IEEE Commun. Lett., 2015, 19, (12), pp. 2218–2221.
    26. 26)
      • 21. Wang, Z., Zheng, S., Ye, Y., et al: ‘Further relaxations of the semidefinite programming approach to sensor network localization’, SIAM J. Optim., 2008, 19, (2), pp. 655–673.
    27. 27)
      • 8. Shikur, B.Y., Weber, T.: ‘Localization in NLOS environments using TOA, AOD, and Doppler-shift’. Workshop on Positioning, Dresden, Germany, 2014, pp. 11–17.
    28. 28)
      • 16. Ash, J.N., Moses, R.L.: ‘Outlier compensation in sensor network self-localization via the EM algorithm’. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, 2005, vol. 4, pp. 749–752.
    29. 29)
      • 35. Aminzadeh, A., Atashgah, M.A.A.: ‘Implementation and performance evaluation of optical flow navigation system under specific conditions for a flying robot’, IEEE Aerosp. Electron. Syst. Mag., 2018, 33, (11), pp. 20–28.
    30. 30)
      • 41. 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.
    31. 31)
      • 17. Yu, X., Peng, J., Wang, Y., et al: ‘Mean shift-based mobile localization method in mixed LOS/NLOS environments for wireless sensor network’, J. Sens., 2017, 2017, pp. 1–8.
    32. 32)
      • 11. Tomic, S., Beko, M., Tuba, M., et al: ‘Target localization in NLOS environments using RSS and TOA measurements’, IEEE Wirel. Commun. Lett., 2018, 7, (6), pp. 1062–1065.
    33. 33)
      • 10. Hua, J., Yin, Y., Lu, W., et al: ‘NLOS identification and positioning algorithm based on localization residual in wireless sensor networks’, Sensors, 2018, 18, (9), p. 2991.
    34. 34)
      • 36. Khriji, S., Houssaini, D.E., Kammoun, I., et al: ‘Energy-efficient routing algorithm based on localization and clustering techniques for agricultural applications’, IEEE Aerosp. Electron. Syst. Mag., 2019, 34, (3), pp. 56–66.
    35. 35)
      • 44. Hamaoui, M.: ‘Non-iterative MDS method for collaborative network localization with sparse range and pointing measurements’, IEEE Trans. Signal Process., 2009, 11, (3), pp. 107–124.
    36. 36)
      • 22. Biswas, P., Liang, T.C., Toh, K.C., et al: ‘Semidefinite programming approaches for sensor network localization with noisy distance measurements’, IEEE Trans. Autom. Sci. Eng., 2006, 3, (4), pp. 360–371.
    37. 37)
      • 2. Chan, Y.T., Hang, H.Y.C., Ching, P.c.: ‘Exact and approximate maximum likelihood localization algorithms’, IEEE Trans. Veh. Technol., 2006, 55, (1), pp. 10–16.
    38. 38)
      • 14. Cheung, K.W., So, H.C., Ma, W.K., et al: ‘A constrained least squares approach to mobile positioning: algorithms and optimality’, EURASIP J. Adv. Signal Process., 2006, 13, (1), pp. 854–858.
    39. 39)
      • 39. Koledoye, M.A., Facchinetti, T., Almeida, L.: ‘Improved MDS-based localization with non-line-of-sight RF links’, J. Intell. Robot. Syst., 2020, 98, (1), pp. 227–237.
    40. 40)
      • 5. Liu, D., Yi, Y., You, Z.: ‘TOA localization in NLOS environments’. Int. Conf. in Communications,Signal Processing, and Systems, Lisbon, Portugal, 2016, pp. 495–503.
    41. 41)
      • 9. Shikur, B.Y., Weber, T.: ‘TDOA/AOD/AOA localization in NLOS environments’. ICASSP 2014 – 2014 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Florence,Italy, 2014, pp. 1–6.
    42. 42)
      • 40. Hirshleifer, J., Hirshleifer, D.: ‘Price theory and applications’ (Prentice-Hall, Englewood Cliffs, NJ, 1980).
    43. 43)
      • 30. Yu, K., Wen, K., Li, Y., et al: ‘A novel nlos mitigation algorithm for UWB localization in harsh indoor environments’, IEEE Trans. Veh. Technol., 2019, 68, (1), pp. 686–699.
    44. 44)
      • 31. Muqaibel, A.H., Amin, M.G., Ahmad, F.: ‘Target localization with a single antenna via directional multipath exploitation’, Int. J. Antennas. Propag., 2015, 2015, pp. 1–10.
    45. 45)
      • 19. Picard, J.S., Weiss, A.J.: ‘Time difference localization in the presence of outliers’, Signal Process., 2012, 92, (10), pp. 2432–2443.
    46. 46)
      • 7. Valentina, B., Paolo, C., Ilaria, D.M.: ‘RSSI-based indoor localization and identification for zigbee wireless sensor networks in smart homes’, IEEE Trans. Instrumen. Meas., 2019, 68, (2), pp. 566–575.
    47. 47)
      • 13. Guvenc, I., Chong, C.C.: ‘A survey on TOA based wireless localization and NLOS mitigation techniques’, IEEE Commun. Surv. Tutorials, 2009, 11, (3), pp. 107–124.
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