Skip to main content
Log in

An Overview on Position Location: Past, Present, Future

  • Published:
International Journal of Wireless Information Networks Aims and scope Submit manuscript

Abstract

Prior to the 21st century, positioning technologies had limited applications including air traffic control, air and sea navigation, satellite communications and related military uses. Today, positioning technologies have deeply merged with daily life and enabled many novel sensors, systems and services. For example, navigation systems are the enablers of road traffic prediction, assisted and autonomous driving, and several aspects of healthcare. They have also facilitated worldwide services provided by companies such as Uber and Lyft. In fact, in many aspects of modern life, localization systems are deemed essential to day-to-day living and are contributing to our general well-being, the economy, and security. Accordingly, position location technologies have become key components of many worldwide industries. These positioning technologies include the Global Positioning System (GPS), WiFi-based indoor localization, cell-phone based localization (including the fusion of GPS, cell-tower based localization and dead-reckoning), and inertial/dead-reckoning techniques. Tracking technologies are also considered key components for localization, as are the more recently integrated concepts of machine learning and artificial intelligence. This paper provides a review of the history of localization, the main technological enablers of localization and assesses the future directions of localization methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. “On the Beam.” http://www.navfltsm.addr.com. Accessed: 2020-09-10.

  2. G. Johnson, M. Wiggins, P. F. Swaszek, L. Hartshorn, and R. Hartnett, “Possible optimizations for the us loran system,” in 2006 IEEE/ION Position, Location, And Navigation Symposium, pp. 695–704, 2006.

  3. O. L. Sentman, “Navy navigation satellite system (transit),” IEEE Aerospace and Electronic Systems Magazine, vol. 2, no. 7, pp. 25–26, 1987.

    Article  Google Scholar 

  4. P. E. Ceruzzi, 2 TWENTIETH-CENTURY NAVIGATING, pp. 19–36. 2018.

  5. C. J. Hegarty and E. Chatre, “Evolution of the global navigation satellitesystem (gnss),” Proceedings of the IEEE, vol. 96, no. 12, pp. 1902–1917, 2008.

    Article  Google Scholar 

  6. K. Maine, P. Anderson, and F. Bayuk, “Communication architecture for gps iii,” in 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720), vol. 3, p. 1539 Vol.3, 2004.

  7. K. P. Maine, P. Anderson, and J. Langer, “Crosslinks for the next-generation gps,” in 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652), vol. 4, pp. 1589–1596, 2003.

  8. P. Steigenberger, S. Thoelert, and O. Montenbruck, “Gps iii vespucci: Results of half a year in orbit,” Advances in Space Research, vol. 66, no. 12, pp. 2773 – 2785, 2020. Scientific and Fundamental Aspects of GNSS - Part 1.

  9. K. Pahlavan, J. Ying, Z. Li, E. Solovey, J. P. Loftus, and Z. Dong, “Rf cloud for cyberspace intelligence,” IEEE Access, vol. 8, pp. 89976–89987, 2020.

    Article  Google Scholar 

  10. S. Venkatesh and R. M. Buehrer, “Non-line-of-sight identification in ultra-wideband systems based on received signal statistics,” IET Microw. Antennas Propag., vol. 1, pp. 1120–1130, Dec. 2007.

    Article  Google Scholar 

  11. Z. Wang and S. A. Zekavat, “Omnidirectional mobile nlos identification and localization via multiple cooperative nodes,” IEEE Transactions on Mobile Computing, vol. 11, no. 12, pp. 2047–2059, 2012.

    Article  Google Scholar 

  12. W. Xu and S. A. R. Zekavat, “Novel high performance mimo-ofdm based measures for nlos identification in time-varying frequency and space selective channels,” IEEE Communications Letters, vol. 16, no. 2, pp. 212–215, 2012.

    Article  Google Scholar 

  13. W. Xu, Z. Wang, and S. A. Zekavat, “Non-line-of-sight identification via phase difference statistics across two-antenna elements,” IET Communications, vol. 5, no. 13, pp. 1814–1822, 2011.

    Article  MathSciNet  MATH  Google Scholar 

  14. D. Li, X. Jia, and J. Zhao, “A novel hybrid fusion algorithm for low-cost gps/ins integrated navigation system during gps outages,” IEEE Access, vol. 8, pp. 53984–53996, 2020.

    Article  Google Scholar 

  15. Z. Wang and S. A. Zekavat, “A novel semidistributed localization via multinode toa-doa fusion,” IEEE Transactions on Vehicular Technology, vol. 58, no. 7, pp. 3426–3435, 2009.

    Article  Google Scholar 

  16. S. T. Goh, S. A. Zekavat, and K. Pahlavan, “Doa-based endoscopy capsule localization and orientation estimation via unscented kalman filter,” IEEE Sensors Journal, vol. 14, no. 11, pp. 3819–3829, 2014.

    Article  Google Scholar 

  17. A. R. Nafchi, S. T. Goh, and S. A. R. Zekavat, “Circular arrays and inertial measurement unit for doa/toa/tdoa-based endoscopy capsule localization: Performance and complexity investigation,” IEEE Sensors Journal, vol. 14, no. 11, pp. 3791–3799, 2014.

    Article  Google Scholar 

  18. S. A. Zekavat and M. Buehrer, Handbook of Position Location; Theory, Practice and Advances, 2nd Edition. Wiley-IEEE Press, December 2018.

  19. S. Fischer, H. Grubeck, A. Kangas, H. Koorapaty, E. Larsson, and P. Lundqvist, “Time of arrival estimation of narrowband tdma signals for mobile positioning,” in Ninth IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (Cat. No.98TH8361), vol. 1, pp. 451–455 vol.1, 1998.

  20. J. W. Powers, “Range trilateration error analysis,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-2, no. 4, pp. 572–585, 1966.

  21. H. L. Groginsky, “Position estimation using only multiple simultaneous range measurements,” IRE Transactions on Aeronautical and Navigational Electronics, vol. ANE-6, no. 3, pp. 178–187, 1959.

  22. H. Staras and S. N. Honickman, “The accuracy of vehicle location by trilateration in a dense urban environment,” IEEE Transactions on Vehicular Technology, vol. 21, no. 1, pp. 38–43, 1972.

    Article  Google Scholar 

  23. W. T. Warren, J. R. Whitten, R. E. Anderson, and M. A. Merigo, “Vehicle location system experiment,” IEEE Transactions on Vehicular Technology, vol. 21, no. 3, pp. 92–101, 1972.

    Article  Google Scholar 

  24. F. J. Berle, “Mixed triangulation/trilateration technique for emitter location,” IEE Proceedings F - Communications, Radar and Signal Processing, vol. 133, no. 7, pp. 638–641, 1986.

    Article  Google Scholar 

  25. D. E. Manolakis, “Efficient solution and performance analysis of 3-d position estimation by trilateration,” IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 4, pp. 1239–1248, 1996.

    Article  Google Scholar 

  26. M. Pent, M. A. Spirito, and E. Turco, “Method for positioning gsm mobile stations using absolute time delay measurements,” Electronics Letters, vol. 33, no. 24, pp. 2019–2020, 1997.

    Article  Google Scholar 

  27. S. Nardi and M. Pachter, “Gps estimation algorithm using stochastic modeling,” in Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), vol. 4, pp. 4498–4502 vol.4, 1998.

  28. A. Aghdaei and S. A. Zekavat, “Novel low-latency, high-resolution and low-cost time synchronisation,” IET Wireless Sensor Systems, vol. 7, no. 6, pp. 178–181, 2017.

    Article  Google Scholar 

  29. M. Jamalabdollahi and S. R. Zekavat, “High resolution toa estimation via optimal waveform design,” IEEE Transactions on Communications, vol. 65, no. 3, pp. 1207–1218, 2017.

    Article  Google Scholar 

  30. M. Jamalabdollahi and S. A. R. Zekavat, “Joint neighbor discovery and time of arrival estimation in wireless sensor networks via ofdma,” IEEE Sensors Journal, vol. 15, no. 10, pp. 5821–5833, 2015.

    Article  Google Scholar 

  31. M. Pourkhaatoun and S. A. Zekavat, “High-resolution low-complexity cognitive-radio-based multiband range estimation: Concatenated spectrum vs. fusion-based,” IEEE Systems Journal, vol. 8, no. 1, pp. 83–92, 2014.

    Article  Google Scholar 

  32. M. Pourkhaatoun and S. A. Zekavat, “High resolution cognitive radio-based concatenated spectrum time-of-arrival estimation,” International Journal of Wireless Information Networks, vol. 19, no. 4, pp. 341–351, Dec. 2012.

    Article  Google Scholar 

  33. M. Pourkhaatoun and S. A. Zekavat, “High-resolution independent component analysis based time-of-arrival estimation for line-of-sight multipath environments,” IET Communications, vol. 5, no. 10, pp. 1440–1452, 2011.

    Article  Google Scholar 

  34. M. Jamalabdollahi, S. Zekavat, and K. Pahlavan, “High-resolution ofdm-based sensor node ranging within in-homogeneous media of human body,” IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2286–2298, 2019.

    Article  Google Scholar 

  35. M. Jamalabdollahi and S. Zekavat, “Toa ranging and layer thickness computation in nonhomogeneous media,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, pp. 742–752, 2017.

    Article  Google Scholar 

  36. S. R. Zekavat, “Wireless local positioning system (wlps),” U.S. Patent 7 489 935, 2009.

  37. H. Tong, J. Pourrostam, and S. A. R. Zekavat, “Lcmv beamforming for a novel wireless local positioning system: Nonstationarity and cyclostationarity analysis,” EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 098243, p. 12 pages, 2007.

  38. H. Tong and S. A. Zekavat, “A novel wireless local positioning system via a merger of ds-cdma and beamforming: Probability-of-detection performance analysis under array perturbations,” IEEE Transactions on Vehicular Technology, vol. 56, no. 3, pp. 1307–1320, 2007.

    Article  Google Scholar 

  39. S. T. Goh, O. Abdelkhalik, and S. A. R. Zekavat, “Constraint estimation of spacecraft positions,” Journal of Guidance, Control, and Dynamics, vol. 35, no. 2, pp. 1307–1320, 2012.

    Article  Google Scholar 

  40. S. T. Goh, O. Abdelkhalik, and S. A. R. Zekavat, “Implementation of differential geometric filter for spacecraft formation orbit estimation,” International Journal of Navigation and Observation, vol. 2012, no. 910496, p. 13 pages, 2012.

  41. S. A. Zekavat, “A novel application for wireless communications in vehicle early warning,” in First IEEE Consumer Communications and Networking Conference, 2004. CCNC 2004, pp. 715–717, 2004.

  42. S. T. Goh, O. Abdelkhalik, and S. A. R. Zekavat, “A weighted measurement fusion kalman filter implementation for uav navigation,” Aerospace Science and Technology, vol. 28, no. 1, pp. 315–323, 2013.

    Article  Google Scholar 

  43. S. T. Goh, O. Abdelkhalik, and S. A. R. Zekavat, “Spacecraft formation orbit estimation using wlps-based localization,” International Journal of Navigation and Observation, vol. 2011, p. 12 pages, 2011.

  44. P. Bahl and V. N. Padmanabhan, “Radar: an in-building rf-based user location and tracking system,” in Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), vol. 2, pp. 775–784 vol.2, 2000.

  45. S. He and S. G. Chan, “Wi-fi fingerprint-based indoor positioning: Recent advances and comparisons,” IEEE Communications Surveys Tutorials, vol. 18, no. 1, pp. 466–490, 2016.

    Article  Google Scholar 

  46. X. Wang, L. Gao, S. Mao, and S. Pandey, “Csi-based fingerprinting for indoor localization: A deep learning approach,” IEEE Transactions on Vehicular Technology, vol. 66, no. 1, pp. 763–776, 2017.

    Google Scholar 

  47. J. Yu, H. M. Saad, and R. M. Buehrer, “Centimeter-level indoor localization using channel state information with recurrent neural networks,” in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 1317–1323, 2020.

  48. J. Zhu and G. D. Durgin, “Indoor/outdoor location of cellular handsets based on received signal strength,” Electronics Letters, vol. 41, no. 1, pp. 24–26, 2005.

  49. H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 6, pp. 1067–1080, 2007.

  50. Z. Wu, C. Li, J. K. Ng, and K. R. p. h. Leung, “Location estimation via support vector regression,” IEEE Transactions on Mobile Computing, vol. 6, no. 3, pp. 311–321, 2007.

  51. W. Xu and S. A. Zekavat, “A high-performance measure for non-line-of-sight identification in mimo-ofdm-based sensor networks,” IEEE Systems Journal, vol. 8, no. 1, pp. 125–130, 2014.

    Article  Google Scholar 

  52. Z. Wang and S. A. Zekavat, “A new toa-doa node localization for mobile ad-hoc networks: Achieving high performance and low complexity,” in 2010 17th International Conference on Telecommunications, pp. 836–842, 2010.

  53. M. I. Silventoinen and T. Rantalainen, “Mobile station emergency locating in gsm,” in 1996 IEEE International Conference on Personal Wireless Communications Proceedings and Exhibition. Future Access, pp. 232–238, 1996.

  54. Li Xiong, “A selective model to suppress nlos signals in angle-of-arrival (aoa) location estimation,” in Ninth IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (Cat. No.98TH8361), vol. 1, pp. 461–465 vol.1, 1998.

  55. İ. Güvenç, C.-C. Chong, F. Watanabe, and H. Inamura, “Nlos identification and weighted least-squares localization for uwb systems using multipath channel statistics,” EURASIP Journal on Advances in Signal Processing, vol. 2008, p. 271984, Aug 2007.

    Article  MATH  Google Scholar 

  56. Li Cong and Weihua Zhuang, “Nonline-of-sight error mitigation in mobile location,” IEEE Transactions on Wireless Communications, vol. 4, no. 2, pp. 560–573, 2005.

    Article  Google Scholar 

  57. M. McGuire, K. N. Plataniotis, and A. N. Venetsanopoulos, “Location of mobile terminals using time measurements and survey points,” IEEE Transactions on Vehicular Technology, vol. 52, no. 4, pp. 999–1011, 2003.

    Article  Google Scholar 

  58. Pi-Chun Chen, “A non-line-of-sight error mitigation algorithm in location estimation,” in WCNC. 1999 IEEE Wireless Communications and Networking Conference (Cat. No.99TH8466), vol. 1, pp. 316–320 vol.1, 1999.

  59. M. P. Wylie and J. Holtzman, “The non-line of sight problem in mobile location estimation,” in Proceedings of ICUPC - 5th International Conference on Universal Personal Communications, vol. 2, pp. 827–831 vol.2, 1996.

  60. S. Venkatraman and J. Caffery, “statistical approach to non-line-of-sight bs identification,” in The 5th International Symposium on Wireless Personal Multimedia Communications, vol. 1, pp. 296–300 vol.1, 2002.

  61. S. Venkatesh and R. M. Buehrer, “Non-line-of-sight identification in ultra-wideband systems based on received signal statistics,” IET Microwaves, Antennas Propagation, vol. 1, no. 6, pp. 1120–1130, 2007.

    Article  Google Scholar 

  62. N. Decarli, D. Dardari, S. Gezici, and A. A. D’Amico, “Los/nlos detection for uwb signals: A comparative study using experimental data,” in IEEE 5th International Symposium on Wireless Pervasive Computing 2010, pp. 169–173, 2010.

  63. Z. Zhou, Z. Yang, C. Wu, L. Shangguan, H. Cai, Y. Liu, and L. M. Ni, “Wifi-based indoor line-of-sight identification,” IEEE Transactions on Wireless Communications, vol. 14, no. 11, pp. 6125–6136, 2015.

    Article  Google Scholar 

  64. J. Choi, W. Lee, J. Lee, J. Lee, and S. Kim, “Deep learning based nlos identification with commodity wlan devices,” IEEE Transactions on Vehicular Technology, vol. 67, no. 4, pp. 3295–3303, 2018.

    Article  Google Scholar 

  65. Z. Wang, W. Xu, and S. A. Zekavat, “A new multi-antenna based los - nlos separation technique,” in 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, pp. 331–336, 2009.

  66. H. J. Jo and S. Kim, “Indoor smartphone localization based on los and nlos identification,” Sensors, vol. 18, no. 11, 2018.

  67. V. Barral, C. J. Escudero, J. A. García-Naya, and R. Maneiro-Catoira, “Nlos identification and mitigation using low-cost uwb devices,” Sensors, vol. 19, no. 16, 2019.

  68. R. M. Buehrer, H. Wymeersch, and R. M. Vaghefi, “Collaborative sensor network localization: Algorithms and practical issues,” Proceedings of the IEEE, vol. 106, no. 6, pp. 1089–1114, 2018.

    Article  Google Scholar 

  69. N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero III, R. L. Moses, and N. S. Correal, “Locating the nodes: Cooperative localization in wireless sensor networks,” IEEE Signal Process. Mag., vol. 22, pp. 54–69, Jul. 2005.

    Article  Google Scholar 

  70. H. Wymeersch, J. Lien, and M. Z. Win, “Cooperative localization in wireless networks,” Proc. IEEE, vol. 97, pp. 427–450, Feb. 2009.

    Article  Google Scholar 

  71. R. M. Buehrer and T. Jia, “Collaborative Position Location,” in Position Location - Theory, Practice and Advances (R. Zekavat and R. Buehrer, eds.), John Wiley and Sons, 2011.

  72. R. M. Vaghefi and R. M. Buehrer, “Cooperative rf pattern matching positioning for lte cellular systems,” in Proc. IEEE PIMRC, pp. 264–269, 2014.

  73. R. M. Vaghefi and R. M. Buehrer, “Improving positioning in lte through collaboration,” in 2014 11th Workshop on Positioning, Navigation and Communication (WPNC), pp. 1–6, March 2014.

  74. B. Huang, Z. Yao, X. Cui, and M. Lu, “Dilution of precision analysis for gnss collaborative positioning,” IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 3401–3415, 2016.

    Article  Google Scholar 

  75. C. Jing, S. Wang, M. Wang, M. Du, L. Zhou, T. Sun, and J. Wang, “A low-cost collaborative location scheme with gnss and rfid for the internet of things,” ISPRS International Journal of Geo-Information, vol. 7, p. 180, May 2018.

    Article  Google Scholar 

  76. W. Wen, X. Bai, G. Zhang, S. Chen, F. Yuan, and L. Hsu, “Multi-agent collaborative gnss/camera/ins integration aided by inter-ranging for vehicular navigation in urban areas,” IEEE Access, vol. 8, pp. 124323–124338, 2020.

    Article  Google Scholar 

  77. J. Schloemann and R. M. Buehrer, “On the value of collaboration in location estimation,” IEEE Transactions on Vehicular Technology, vol. 65, pp. 3585–3596, May 2016.

    Article  Google Scholar 

  78. Y. Shen, H. Wymeersch, and M. Z. Win, “Fundamental limits of wideband localization part II: Cooperative networks,” IEEE Trans. Inf. Theory, vol. 56, pp. 4981–5000, Oct. 2010.

    Article  MathSciNet  MATH  Google Scholar 

  79. G. Giorgetti, S. K. S. Gupta, and G. Manes, “Understanding the limits of rf-based collaborative localization,” IEEE/ACM Transactions on Networking, vol. 19, pp. 1638–1651, Dec 2011.

    Article  Google Scholar 

  80. D. B. Jourdan and N. Roy, “Optimal sensor placement for agent localization,” ACM Transactions on Sensor Networks (TOSN), vol. 4, pp. 128–139, Apr. 2008.

    Article  Google Scholar 

  81. K. Das and H. Wymeersch, “Censoring for Bayesian cooperative positioning in dense wireless networks,” IEEE J. Sel. Areas Commun., vol. 30, pp. 1835–1842, Oct. 2012.

    Article  Google Scholar 

  82. T. Jia and R. M. Buehrer, “On the optimal performance of collaborative position location,” IEEE Trans. Wireless Commun., vol. 9, pp. 374–383, Jan. 2010.

  83. B. Zhou and Q. Chen, “On particle-assisted stochastic search mechanism in wireless cooperative localization,” IEEE Transactions on Wireless Communication, vol. 15, pp. 4765–4777, July 2016.

    Google Scholar 

  84. L. Doherty, K. S. J. pister, and L. El Ghaoui, “Convex position estimation in wireless sensor networks,” in Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213), vol. 3, pp. 1655–1663 vol.3, 2001.

  85. S. Lederer, Y. Wang, and J. Gao, “Connectivity-based localization of large scale sensor networks with complex shape,” in Proceedings of IEEE Conference on Computer Communications (INFOCOM), pp. 789–797, April 2008.

  86. M. Jin, S. Xia, H. Wu, and X. Gu, “Scalable and fully distributed localization with mere connectivity,” in 2011 Proceedings IEEE INFOCOM, pp. 3164–3172, 2011.

  87. G. Mao, B. Fidan, and B. D. O. Anderson, “Wireless sensor network localization techniques,” International Journal of Computer and Telecommunications Networking, vol. 51, no. 10, pp. 2529–2553, 2007.

    MATH  Google Scholar 

  88. R. M. Vaghefi, J. Schloemann, and R. M. Buehrer, “Nlos mitigation in toa-based localization using semidefinite programming,” in 2013 10th Workshop on Positioning, Navigation and Communication (WPNC), pp. 1–6, 2013.

  89. D. Niculescu and B. Nath, “Ad hoc positioning system (APS),” in Proceedings of IEEE GLOBECOM, pp. 2926–2931, 2001.

  90. J. A. Costa, N. Patwari, and A. O. Hero III, “Distributed weighted-multidimensional scaling for node localization in sensor networks,” ACM Transactions on Sensor Networks, vol. 2, no. 1, pp. 39–64, 2006.

    Article  Google Scholar 

  91. T. Jia and R. Buehrer, “A set-theoretic approach to collaborative position location for wireless networks,” IEEE Trans. Mobile Comput., vol. 10, pp. 1264–1275, Sep. 2011.

    Article  Google Scholar 

  92. R. M. Buehrer, S. Venkatesh, and T. Jia, “Mitigation of the propagation of localization error using multi-hop bounding,” in Proceedings of IEEE Wireless Communications and Networking Conference (WCNC), pp. 3009–3014, Apr. 2008.

  93. D. Kirchner, “Two-way time transfer via communication satellites,” Proceedings of the IEEE, vol. 79, no. 7, pp. 983–990, 1991.

    Article  Google Scholar 

  94. Y. Ye, P. Swar, K. Pahlavan, and K. Ghaboosi, “Accuracy of RSS-based RF localization in multi-capsule endoscopy,” Wireless Inf Networks, vol. 19, pp. 229–238, 2012.

    Article  Google Scholar 

  95. R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276–280, 1986.

    Article  Google Scholar 

  96. D. Salvati, C. Drioli, and G. L. Foresti, “A low-complexity robust beamforming using diagonal unloading for acoustic source localization,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 3, pp. 609–622, 2018.

    Article  Google Scholar 

  97. J. P. Dmochowski, J. Benesty, and S. Affes, “A generalized steered response power method for computationally viable source localization,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 8, pp. 2510–2526, 2007.

    Article  Google Scholar 

  98. A. L. Gilbert, M. K. Giles, G. M. Flachs, R. B. Rogers, and U. Y. Hsun, “A real-time video tracking system,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, no. 1, pp. 47–56, 1980.

  99. B. Mukhopadhyay, S. Sarangi, S. Srirangarajan, and S. Kar, “Indoor localization using analog output of pyroelectric infrared sensors,” in 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6, 2018.

  100. S. Ojha and S. Sakhare, “Image processing techniques for object tracking in video surveillance- a survey,” in 2015 International Conference on Pervasive Computing (ICPC), pp. 1–6, 2015.

  101. A. Mundy, “Submarine sound-direction finder,” 23 Aug. 1904. US Patent 768,573.

  102. A. B. Wood, A textbook of sound, p. 263. London: G. Bell and Sons. Ltd, 1946.

  103. S. R. Watson-Watt, “Radar defense today - and tomorrow,” Foreign Affairs (pre-1986), vol. 32, pp. 230–243, Jan 1954.

  104. “Ball tracker speeds missile target detection,” Electrical Engineering, vol. 81, no. 7, pp. 545–545, 1962.

  105. “Giant tracker watches missiles that are 300 miles away,” Electrical Engineering, vol. 75, no. 12, pp. 1136–1137, 1956.

  106. R. E. Kalman, “A new approach to linear filtering and prediction problems,” J. Basic Eng., vol. 82, no. 1, pp. 35–45, Mar 1960.

    Article  MathSciNet  Google Scholar 

  107. S. N. Salinger and J. J. Brandstatter, “Application of recursive estimation and kalman filtering to doppler tracking,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-6, no. 4, pp. 585–592, 1970.

  108. J. S. Thorp, “Optimal tracking of maneuvering targets,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-9, no. 4, pp. 512–519, 1973.

  109. F. El-Hawary, F. Aminzadeh, and G. A. N. Mbamalu, “The generalized kalman filter approach to adaptive underwater target tracking,” IEEE Journal of Oceanic Engineering, vol. 17, no. 1, pp. 129–137, 1992.

    Article  Google Scholar 

  110. S. J. Julier and J. K. Uhlmann, “New extension of the kalman filter to nonlinear systems,” in SPIE Proceedings, vol. 3068, p. 12, 1997.

  111. B. Cui and J. Zhang, “The improved ensemble kalman filter for multisensor target tracking,” in 2008 International Symposium on Information Science and Engineering, vol. 1, pp. 263–265, 2008.

  112. L. Khalil and P. Jung, “Spherical simplex unscented kalman filter for rssi-based wlan ieee 802.11n positioning and tracking,” in 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 2094–2098, 2015.

  113. O. Straka, J. Duník, and M. Šimandl, “Randomized unscented kalman filter in target tracking,” in 2012 15th International Conference on Information Fusion, pp. 503–510, 2012.

  114. W. Li and Y. Jia, “Location of mobile station with maneuvers using an imm-based cubature kalman filter,” IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4338–4348, 2012.

    Article  Google Scholar 

  115. J. Guo, H. Zhang, Y. Sun, and R. Bie, “Square-root unscented kalman filtering-based localization and tracking in the internet of things,” Personal and Ubiquitous Computing, vol. 18, pp. 987–996, 2014.

    Article  Google Scholar 

  116. J. Chen, J. Li, S. Yang, and F. Deng, “Weighted optimization-based distributed kalman filter for nonlinear target tracking in collaborative sensor networks,” IEEE Transactions on Cybernetics, vol. 47, no. 11, pp. 3892–3905, 2017.

    Article  Google Scholar 

  117. B. Tomasini, M. Gauvrit, and B. Siffredi, “Bayesian adaptive filters for multiple maneuvering target tracking with measurements of uncertain origin,” in Proceedings of the 28th IEEE Conference on Decision and Control,, pp. 1397–1399 vol.2, 1989.

  118. Xiao-Jiao Tao, Cai-Rong Zou, and Zhen-Ya He, “Passive target tracking using maximum likelihood estimation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 32, no. 4, pp. 1348–1354, 1996.

    Article  Google Scholar 

  119. P. R. Kalata, \(\alpha -\beta\) target tracking systems: a survey,” in 1992 American Control Conference, pp. 832–836, 1992.

  120. K. Granstrom, C. Lundquist, and O. Orguner, “Extended target tracking using a gaussian-mixture phd filter,” IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 4, pp. 3268–3286, 2012.

    Article  Google Scholar 

  121. D. B. Ward, E. A. Lehmann, and R. C. Williamson, “Particle filtering algorithms for tracking an acoustic source in a reverberant environment,” IEEE Transactions on Speech and Audio Processing, vol. 11, no. 6, pp. 826–836, 2003.

    Article  Google Scholar 

  122. K. Wu, V. G. Reju, A. W. H. Khong, and S. T. Goh, “Swarm intelligence based particle filter for alternating talker localization and tracking using microphone arrays,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 6, pp. 1384–1397, 2017.

    Article  Google Scholar 

  123. S. Mori, Chee-Yee Chong, E. Tse, and R. Wishner, “Tracking and classifying multiple targets withouta prioriidentification,” IEEE Transactions on Automatic Control, vol. 31, no. 5, pp. 401–409, 1986.

  124. S. S. Blackman, “Multiple hypothesis tracking for multiple target tracking,” IEEE Aerospace and Electronic Systems Magazine, vol. 19, no. 1, pp. 5–18, 2004.

    Article  Google Scholar 

  125. A. Levy, S. Gannot, and E. A. P. Habets, “Multiple-hypothesis extended particle filter for acoustic source localization in reverberant environments,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 6, pp. 1540–1555, 2011.

    Article  Google Scholar 

  126. Y. Oualil, F. Faubel, and D. Klakow, “A multiple hypothesis gaussian mixture filter for acoustic source localization and tracking,” in IWAENC 2012; International Workshop on Acoustic Signal Enhancement, pp. 1–4, 2012.

  127. T. S. Kelso, “Validation of SGP4 and IS-GPS-200D against GPS precision ephemerides,” in 17th AAS/AIAA Space Flight Mechanics Conference, (Sedona, Arizona), 2007. AAS 07-127.

  128. M. R.Pearlman, J. J. Degnan, and J. M. Bosworth, “The international laser ranging service,” Advances in Space Research, vol. 30, no. 2, pp. 135–143, 2002.

    Article  Google Scholar 

  129. K. Werner, J. Bredemeyer, and T. Delovski, “Ads-b over satellite: Global air traffic surveillance from space,” in 2014 Tyrrhenian International Workshop on Digital Communications - Enhanced Surveillance of Aircraft and Vehicles (TIWDC/ESAV), pp. 47–52, 2014.

  130. S. Yu, L. Chen, C. Fan, G. Ding, Y. Zhao, and X. Chen, “Integrated antenna and receiver system with self-calibrating digital beamforming for space-based ads-b,” Acta Astronautica, vol. 170, pp. 480–486, 2020.

    Article  Google Scholar 

  131. F. Mazzarella, M. Vespe, and C. Santamaria, “Sar ship detection and self-reporting data fusion based on traffic knowledge,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 8, pp. 1685–1689, 2015.

    Article  Google Scholar 

  132. Y. Liu, L. Yao, W. Xiong, and Z. Zhou, “Gf-4 satellite and automatic identification system data fusion for ship tracking,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 2, pp. 281–285, 2019.

    Article  Google Scholar 

  133. C. Carthel, S. Coraluppi, R. Grasso, and P. Grignan, “Fusion of AIS, RADAR, and SAR data for maritime surveillance,” in Image and Signal Processing for Remote Sensing XIII (L. Bruzzone, ed.), vol. 6748, pp. 320 – 327, International Society for Optics and Photonics, SPIE, 2007.

  134. M. Guerriero, P. Willett, S. Coraluppi, and C. Carthel, “Radar/ais data fusion and sar tasking for maritime surveillance,” in 2008 11th International Conference on Information Fusion, pp. 1–5, 2008.

  135. F. Lázaro, R. Raulefs, W. Wang, F. Clazzer, and S. Plass, “VHF data exchange system (VDES): an enabling technology for maritime communications,” CEAS Space Journal, vol. 11, no. 1, pp. 55–63, 2019.

    Article  Google Scholar 

  136. W. E. Williams, “Submarine detecting and destroying apparatus,” 18 May 1917. US Patent 1,344,074.

  137. D. M. Dwyer, “Real time kalman filtering for torpedo range tracking,” Master’s thesis, Naval Postgraduate School, December 1978.

  138. G. E. Monan and D. L. Thorne, “Sonic tags attached to alaska king crab,” Mar. Fish. Rev., vol. 35, no. 7, pp. 18–21, 1973.

    Google Scholar 

  139. Z. D. Deng, M. A. Weiland, T. Fu, T. A. Seim, B. L. LaMarche, E. Y. Choi, T. J. Carlson, and M. B. Eppard, “A cabled acoustic telemetry system for detecting and tracking juvenile salmon: Part 2. three-dimensional tracking and passage outcomes,” Sensors, vol. 11, no. 6, pp. 5661–5676, 2011.

    Article  Google Scholar 

  140. J. D. Winter, V. B. Kuechle, D. B. Siniff, and J. R. Tester, “quipment and methods for radio tracking freshwater fish,” tech. rep., University of Minnesota, Institute of Agriculture, St. Paul, 1978. Miscellaneous Report 152.

  141. J. Luo, Y. Han, and L. Fan, “Underwater acoustic target tracking: A review,” Sensors, vol. 18, p. 112, Jan 2018.

    Article  Google Scholar 

  142. C. Spampinato, Y.-H. Chen-Burger, G. Nadarajan, and R. B. Fisher, “Detecting, tracking and counting fish in low quality unconstrained underwater videos,” in Proceedings of the Third International Conference on Computer Vision Theory and Applications, vol. 2, (Funchal, Madeira, Portugal), p. 6, Institute for Systems and Technologies of Information, Control and Communication, 2008.

  143. T. Chiang, K. Ou, J. Qiu, and Y. Tseng, “Pedestrian tracking by acoustic doppler effects,” IEEE Sensors Journal, vol. 19, no. 10, pp. 3893–3901, 2019.

    Article  Google Scholar 

  144. W. Kang and Y. Han, “Smartpdr: Smartphone-based pedestrian dead reckoning for indoor localization,” IEEE Sensors Journal, vol. 15, no. 5, pp. 2906–2916, 2015.

    Article  Google Scholar 

  145. M. Basso, M. Galanti, G. Innocenti, and D. Miceli, “Triggered ins/gnss data fusion algorithms for enhanced pedestrian navigation system,” IEEE Sensors Journal, vol. 20, no. 13, pp. 7447–7459, 2020.

    Article  Google Scholar 

  146. H. Xing, J. Li, B. Hou, Y. Zhang, and M. Guo, “Pedestrian stride length estimation from imu measurements and ann based algorithm,” Journal of Sensors, vol. 2017, p. 11, 2017.

    Article  Google Scholar 

  147. R. Jirawimut, P. Ptasinski, V. Garaj, F. Cecelja, and W. Balachandran, “A method for dead reckoning parameter correction in pedestrian navigation system,” IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 1, pp. 209–215, 2003.

    Article  Google Scholar 

  148. S. K. Park and Y. S. Suh, “A zero velocity detection algorithm using inertial sensors for pedestrian navigation systems,” Sensors, vol. 10, p. 9163–9178, Oct 2010.

    Article  Google Scholar 

  149. K. Song Gong and H. Chen, “Robust indoor speaker localization in the circular harmonic domain,” IEEE Transactions on Industrial Electronics, pp. 1–1, 2020.

  150. X. Zhong, A. B. Premkumar, and H. Wang, “Multiple wideband acoustic source tracking in 3-d space using a distributed acoustic vector sensor array,” IEEE Sensors Journal, vol. 14, no. 8, pp. 2502–2513, 2014.

    Article  Google Scholar 

  151. M. F. Fallon and S. Godsill, “Acoustic source localization and tracking using track before detect,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 6, pp. 1228–1242, 2010.

    Article  Google Scholar 

  152. Z. Tian, W. Liu, and X. Ru, “Multi-target localization and tracking using tdoa and aoa measurements based on gibbs-glmb filtering,” Sensors, vol. 19, p. 5437, Dec 2019.

    Article  Google Scholar 

  153. Wing-Kin Ma, Ba-Ngu Vo, S. S. Singh, and A. Baddeley, “Tracking an unknown time-varying number of speakers using tdoa measurements: a random finite set approach,” IEEE Transactions on Signal Processing, vol. 54, no. 9, pp. 3291–3304, 2006.

  154. R. Sarcinelli, R. Guidolini, V. B. Cardoso, T. M. Paixão, R. F. Berriel, P. Azevedo, A. F. De Souza, C. Badue, and T. Oliveira-Santos, “Handling pedestrians in self-driving cars using image tracking and alternative path generation with frenét frames,” Computers & Graphics, vol. 84, pp. 173–184, 2019.

    Article  Google Scholar 

  155. D. Fischer, R. Schreiber, D. Levi, and R. Eliakim, “Capsule endoscopy: the localization system,” Gastrointestinal Endoscopy Clinics of North America, vol. 14, no. 1, pp. 25 – 31, 2004. Wireless Capsule Endoscopy.

  156. X. Du, F. Lao, and G. Teng, “A sound source localisation analytical method for monitoring the abnormal night vocalisations of poultry,” Sensors, vol. 18, p. 2906, Sep 2018.

    Article  Google Scholar 

  157. F. Wen, H. Wymeersch, B. Peng, W. P. Tay, H. C. So, and D. Yang, “A survey on 5g massive mimo localization,” Digital Signal Processing, vol. 94, pp. 21 – 28, 2019. Special Issue on Source Localization in Massive MIMO.

  158. A. Decurninge, L. G. Ordóñez, P. Ferrand, H. Gaoning, L. Bojie, Z. Wei, and M. Guillaud, “Csi-based outdoor localization for massive mimo: Experiments with a learning approach,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6, 2018.

  159. J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, “Deep convolutional neural networks for massive mimo fingerprint-based positioning,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–6, 2017.

  160. X. Sun, X. Gao, G. Y. Li, and W. Han, “Single-site localization based on a new type of fingerprint for massive mimo-ofdm systems,” IEEE Transactions on Vehicular Technology, vol. 67, no. 7, pp. 6134–6145, 2018.

    Article  Google Scholar 

  161. L. Zhang and X. Zhang, “Mimo channel estimation and equalization using three-layer neural networks with feedback,” Tsinghua Science & Technology, vol. 12, no. 6, pp. 658–662, 2007.

    Article  Google Scholar 

  162. D. Neumann, T. Wiese, and W. Utschick, “Learning the mmse channel estimator,” IEEE Transactions on Signal Processing, vol. 66, no. 11, pp. 2905–2917, 2018. cited By 34.

  163. V. K. Mago and N. Bhatia, eds., Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies, ch. Estimation of MIMO Wireless Channels Using Artificial Neural Networks, p. 509–543. IGI Global, 2012.

  164. M. Bhuyan and K. Sarma, “Mimo-ofdm channel tracking using a dynamic ann topology,” World Academy of Science, Engineering and Technology, vol. 71, pp. 1321–1327, 2012. cited By 12.

  165. M. Z. Comiter, M. B. Crouse, and H. T. Kung, “A data-driven approach to localization for high frequency wireless mobile networks,” in GLOBECOM 2017 - 2017 IEEE Global Communications Conference, pp. 1–7, 2017.

  166. K. N. R. S. V. Prasad, E. Hossain, V. K. Bhargava, and S. Mallick, “Analytical approximation-based machine learning methods for user positioning in distributed massive mimo,” IEEE Access, vol. 6, pp. 18431–18452, 2018.

    Article  Google Scholar 

  167. G. Mao and B. Fidan, eds., Localization Algorithms and Strategies for Wireless Sensor Networks, ch. Machine Learning Based Localization, p. 302–320. Information Science Reference (an imprint of IGI Global), 2009.

  168. G. Bhatti, “Machine learning based localization in large-scale wireless sensor networks,” Sensors, vol. 18, no. 12, 4179, 2018.

    Article  Google Scholar 

  169. X. Nguyen, M. I. Jordan, and B. Sinopoli, “A kernel-based learning approach to ad hoc sensor network localization,” ACM Trans. Sen. Netw., vol. 1, p. 134–152, Aug. 2005.

  170. I. Ahriz, Y. Oussar, B. Denby, and G. Dreyfus, “Full-band gsm fingerprints for indoor localization using a machine learning approach,” International Journal of Navigation and Observation, vol. 2010, Article ID 497829, 7 pages, 2010.

  171. D. A. Tran and T. Nguyen, “Localization in wireless sensor networks based on support vector machines,” IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 7, pp. 981–994, 2008.

    Article  Google Scholar 

  172. S. Maranò, W. M. Gifford, H. Wymeersch, and M. Z. Win, “Nlos identification and mitigation for localization based on uwb experimental data,” IEEE Journal on Selected Areas in Communications, vol. 28, no. 7, pp. 1026–1035, 2010.

    Article  Google Scholar 

  173. T. Lin, S. Fang, W. Tseng, C. Lee, and J. Hsieh, “A group-discrimination-based access point selection for wlan fingerprinting localization,” IEEE Transactions on Vehicular Technology, vol. 63, no. 8, pp. 3967–3976, 2014.

    Article  Google Scholar 

  174. J. Hong and T. Ohtsuki, “Signal eigenvector-based device-free passive localization using array sensor,” IEEE Transactions on Vehicular Technology, vol. 64, no. 4, pp. 1354–1363, 2015.

    Article  Google Scholar 

  175. T. Van Nguyen, Y. Jeong, H. Shin, and M. Z. Win, “Machine learning for wideband localization,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 7, pp. 1357–1380, 2015.

    Article  Google Scholar 

  176. S. Bozkurt, G. Elibol, S. Gunal, and U. Yayan, “A comparative study on machine learning algorithms for indoor positioning,” in 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), pp. 1–8, 2015.

  177. A. H. Salamah, M. Tamazin, M. A. Sharkas, and M. Khedr, “An enhanced wifi indoor localization system based on machine learning,” in 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8, 2016.

  178. W. Zhang, K. Liu, W. Zhang, Y. Zhang, and J. Gu, “Deep neural networks for wireless localization in indoor and outdoor environments,” Neurocomputing, vol. 194, pp. 279–287, 2016.

    Article  Google Scholar 

  179. D. Fahed and R. Liu, “Wi-fi-based localization in dynamic indoor environment using a dynamic neural network,” International Journal of Machine Learning and Computing, vol. 3, no. 1, pp. 127–131, 2013.

    Article  Google Scholar 

  180. G. Félix, M. Siller, and E. N. Álvarez, “A fingerprinting indoor localization algorithm based deep learning,” in 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 1006–1011, 2016.

  181. A. A. Abdallah, S. S. Saab, and Z. M. Kassas, “A machine learning approach for localization in cellular environments,” in 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), pp. 1223–1227, 2018.

  182. X. Wang, L. Gao, and S. Mao, “Csi phase fingerprinting for indoor localization with a deep learning approach,” IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1113–1123, 2016.

    Article  Google Scholar 

  183. K. Sabanci, E. Yigit, D. Ustun, A. Toktas, and M. F. Aslan, “Wifi based indoor localization: Application and comparison of machine learning algorithms,” in 2018 XXIIIrd International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), pp. 246–251, 2018.

  184. M. Petric, A. Neskovic, N. Neskovic, and M. Borenovic, “Indoor localization using multi-operator public land mobile networks and support vector machine learning algorithms,” Wireless Personal Communication, vol. 104, p. 1573–1597, 2019.

    Article  Google Scholar 

  185. O. Bin Tariq, M. T. Lazarescu, J. Iqbal, and L. Lavagno, “Performance of machine learning classifiers for indoor person localization with capacitive sensors,” IEEE Access, vol. 5, pp. 12913–12926, 2017.

  186. D. A. Bibb, Z. Yun, and M. F. Iskander, “Machine learning for source localization in urban environments,” in MILCOM 2016 - 2016 IEEE Military Communications Conference, pp. 401–405, 2016.

  187. E. L. Berz, D. A. Tesch, and F. P. Hessel, “Rfid indoor localization based on support vector regression and k-means,” in 2015 IEEE 24th International Symposium on Industrial Electronics (ISIE), pp. 1418–1423, 2015.

  188. A. Wille, M. Broll, and S. Winter, “Phase difference based rfid navigation for medical applications,” in 2011 IEEE International Conference on RFID, pp. 98–105, 2011.

  189. L. Yang, Q. Liu, J. Xu, J. Hu, and T. Song, “An indoor rfid location algorithm based on support vector regression and particle swarm optimization,” in 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), pp. 1–6, 2018.

  190. L. N. Balico, A. A. F. Loureiro, E. F. Nakamura, R. S. Barreto, R. W. Pazzi, and H. A. B. F. Oliveira, “Localization prediction in vehicular ad hoc networks,” IEEE Communications Surveys Tutorials, vol. 20, no. 4, pp. 2784–2803, 2018.

    Article  Google Scholar 

  191. S. Tuncer and T. Tuncer, “Indoor localization with bluetooth technology using artificial neural networks,” in 2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES), pp. 213–217, 2015.

  192. F. Yu, M. Jiang, J. Liang, X. Qin, M. Hu, P. Tao, and X. Hu, “5 g wifi signal-based indoor localization system using cluster -nearest neighbor algorithm,” International Journal of Distributed Sensor Networks, vol. 2014, 12 2014.

    Google Scholar 

  193. C. Li, Z. Qiu, and C. Liu, “An improved weighted k-nearest neighbor algorithm for indoor positioning,” Wireless Personal Communications, vol. 96, pp. 2239–2251, 2017.

    Article  Google Scholar 

  194. R. Faragher and R. K. Harle, “An analysis of the accuracy of bluetooth low energy for indoor positioning applications,” 2014.

  195. R. Faragher and R. Harle, “Location fingerprinting with bluetooth low energy beacons,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 11, pp. 2418–2428, 2015.

    Article  Google Scholar 

  196. H. A. Dell, “Introduction to radar systems.merrill i. skolnik. mcgraw-hill book co., london and new york. 1962. 648 pp. illustrated. £5 12s. 6d.,” The Journal of the Royal Aeronautical Society, vol. 67, no. 629, p. 313–313, 1963.

  197. T. Yanagisawa, K. Yamamoto, and Y. Kubota, “Development of a laser radar system for automobiles,” in SAE Technical Paper, SAE International, 02 1992.

  198. H. Rohling, M. . Meinecke, K. Mott, and L. Urs, “Research activities in automotive radar,” in Fourth International Kharkov Symposium ’Physics and Engineering of Millimeter and Sub-Millimeter Waves’. Symposium Proceedings (Cat. No.01EX429), vol. 1, pp. 48–51 vol.1, 2001.

  199. A. Torabi and S. A. Zekavat, “Near-ground channel modeling for distributed cooperative communications,” IEEE Transactions on Antennas and Propagation, vol. 64, no. 6, pp. 2494–2502, 2016.

    Article  MathSciNet  MATH  Google Scholar 

  200. S. L. Javali, A. Torabi, and S. A. R. Zekavat, “Snow covered forest channel modeling for near-ground wireless sensor networks,” in 2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE), pp. 69–74, 2017.

  201. L. Zheng, M. Lops, and X. Wang, “Adaptive interference removal for uncoordinated radar/communication coexistence,” IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 45–60, 2018.

    Article  Google Scholar 

  202. A. Koelle, S. Depp, and R. Freyman, “Short-range radio-telemetry for electronic identification using modulated RF backscatter,” Proc. of the IEEE, vol. 63, pp. 1260–1261, August 1975.

    Article  Google Scholar 

  203. K. Finkenzeller, RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification, and Near-Field Communication. John Wiley and Sons, 3rd ed., 2010.

  204. L. M. Ni, Y. Liu, Y. Lau, and A. P. Patil, “LANDMARC: indoor location sensing using active RFID,” in Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)., pp. 407–415, March 2003.

  205. J. Zhu, Indoor/Outdoor Location of Cellular Handsets Based on Received Signal Strength. PhD thesis, Georgia Institute of Technology, Atlanta, 2006.

  206. D. Arnitz, K. Witrisal, and U. Muehlmann, “Multifrequency continuous-wave radar approach to ranging in passive UHF RFID,” IEEE Transactions on Microwave Theory and Techniques, vol. 57, pp. 1398–1405, May 2009.

    Article  Google Scholar 

  207. P. V. Nikitin, R. Martinez, S. Ramamurthy, H. Leland, G. Spiess, and K. V. S. Rao, “Phase based spatial identification of UHF RFID tags,” in 2010 IEEE International Conference on RFID (IEEE RFID 2010), pp. 102–109, April 2010.

  208. C. Zhou and J. D. Griffin, “Phased-based composite ranging for backscatter rf tags: System analysis and measurements,” IEEE Transactions on Antennas and Propagation, vol. 66, pp. 4202–4212, Aug 2018.

    Article  Google Scholar 

  209. M. Akbar, D. Taylor, and G. Durgin, “Hybrid inertial microwave reflectometry for mm-scale tracking in RFID systems,” IEEE Trans. Wireless Comm., vol. 14, pp. 6805–6814, Dec 2015.

    Article  Google Scholar 

  210. M. B. Akbar, Hybrid Inertial Microwave Reflectometry for mm-scale Tracking in RFID Systems. PhD thesis, Georgia Institute of Technology, Atlanta, 2016.

  211. Q. Yang, D. G. Taylor, and G. D. Durgin, “Kalman filter based localization and tracking estimation for HIMR RFID systems,” in 2018 IEEE International Conference on RFID (RFID), pp. 1–5, April 2018.

  212. P. Nikitin, K. Rao, and S. Lam, “UHF RFID Tag Characterization: Overview and State-of-the-Art,” in AMTA 34th Annual Meeting and Symposium, (Seattle WA), 2012.

  213. G. Durgin, “RF Thermoelectric Generation for Passive RFID,” in IEEE RFID Conference, (Orlando FL), April 2016.

  214. S. Hemour and K. Wu, “Radio-Frequency Rectifier for Electromagnetic Energy Harvesting: Development Path and Future Outlook,” Proceedings of the IEEE, vol. 102, pp. 1667–1691, Nov 2014.

    Article  Google Scholar 

  215. J. Kimionis, A. Georgiadis, A. Collado, and E. Tentzeris, “Enhancement of RF Tag Backscatter Efficiency With Low-Power Reflection Amplifiers,” IEEE Transactions on Microwave Theory and Techniques, vol. 62, Dec 2014.

  216. F. Amato, C. W. Peterson, M. B. Akbar, and G. D. Durgin, “Long range and low powered RFID tags with tunnel diode,” in 2015 IEEE International Conference on RFID Technology and Applications (RFID-TA), pp. 182–187, Sept 2015.

  217. F. Amato, H. M. Torun, and G. D. Durgin, “Beyond the limits of classic backscattering communications: A quantum tunneling RFID tag,” in 2017 IEEE International Conference on RFID (RFID), pp. 20–25, May 2017.

  218. C. Qi, F. Amato, M. Alhassoun, and G. D. Durgin, “Breaking the range limit of rfid localization: Phase-based positioning with tunneling tags,” in 2019 IEEE International Conference on RFID (RFID), April 2019.

  219. E. F. Schubert and J. K. Kim, “Solid-state light sources getting smart,” Science, vol. 308, no. 5726, pp. 1274–1278, 2005.

    Article  Google Scholar 

  220. L. Lovisolo, M. P. Tcheou, and F. R. Aacute; vila, “Visible light-based communication and localization,” Handbook of Position Location: Theory, Practice, and Advances, Second Edition, pp. 1121–1164, 2018.

  221. R. K. Gebel, “Optical radar and passive optoelectronic ranging,” The Ohio Journal of Science, no. 5, 1966.

  222. M. Landry, “GB-60A light detecting and ranging system (LIDAR).,” tech. rep., Sandia Corp., Albuquerque, N. Mex., 1967.

  223. M. D. Altschuler, B. R. Altschuler, and J. Taboada, “Laser electro-optic system for rapid three-dimensional (3-D) topographic mapping of surfaces,” Optical Engineering, vol. 20, no. 6, p. 206953, 1981.

    Article  Google Scholar 

  224. A. N. Golubev and A. M. Chekhovsky, “Three-color optical range finding,” Applied optics, vol. 33, no. 31, pp. 7511–7517, 1994.

    Article  Google Scholar 

  225. I. Edwards, “Using photodetectors for position sensing,” Sensors, vol. 5, no. 12, pp. 26–32, 1988.

    Google Scholar 

  226. A. Makynen, T. Rahkonen, and J. Kostamovaara, “A CMOS binary position-sensitive photodetector (PSD) array,” in Proceedings of CICC 97-Custom Integrated Circuits Conference, pp. 279–282, IEEE, 1997.

  227. R. Taylor and P. J. Probert, “Range finding and feature extraction by segmentation of images for mobile robot navigation,” in Proceedings of IEEE International Conference on Robotics and Automation, vol. 1, pp. 95–100, IEEE, 1996.

  228. P. Saint-Marc, J.-L. Jezouin, and G. Medioni, “A versatile PC-based range finding system,” IEEE Transactions on Robotics and Automation, vol. 7, no. 2, pp. 250–256, 1991.

    Article  Google Scholar 

  229. K. Römer, “The lighthouse location system for smart dust,” in Proceedings of the 1st international conference on Mobile systems, applications and services, pp. 15–30, 2003.

  230. J. Randall, O. Amft, J. Bohn, and M. Burri, “Luxtrace: indoor positioning using building illumination,” Personal and ubiquitous computing, vol. 11, no. 6, pp. 417–428, 2007.

    Article  Google Scholar 

  231. N. Ravi and L. Iftode, “Fiatlux: Fingerprinting rooms using light intensity,” in Pervasive, 2007.

  232. A. R. Jiménez, F. Zampella, and F. Seco, “Light-matching: A new signal of opportunity for pedestrian indoor navigation,” in International conference on indoor positioning and indoor navigation, pp. 1–10, IEEE, 2013.

  233. Q. Xu, R. Zheng, and S. Hranilovic, “IDyLL: Indoor localization using inertial and light sensors on smartphones,” in Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 307–318, 2015.

  234. B. Xie, K. Chen, G. Tan, M. Lu, Y. Liu, J. Wu, and T. He, “LIPS: A light intensity-based positioning system for indoor environments,” ACM Transactions on Sensor Networks (TOSN), vol. 12, no. 4, pp. 1–27, 2016.

    Article  Google Scholar 

  235. G. K. Pang and H. S. Liu, “LED location beacon system based on processing of digital images,” IEEE Transactions on Intelligent Transportation Systems, vol. 2, no. 3, pp. 135–150, 2001.

    Article  Google Scholar 

  236. H. S. Liu and G. Pang, “Positioning beacon system using digital camera and LEDs,” IEEE Transactions on Vehicular Technology, vol. 52, no. 2, pp. 406–419, 2003.

    Article  Google Scholar 

  237. X. Liu, H. Makino, S. Kobayashi, and Y. Maeda, “Design of an indoor self-positioning system for the visually impaired-simulation with rfid and bluetooth in a visible light communication system,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1655–1658, IEEE, 2007.

  238. C. Zhang and X. Zhang, “Litell: Robust indoor localization using unmodified light fixtures,” in Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pp. 230–242, 2016.

  239. N. Rajagopal, P. Lazik, and A. Rowe, “Visual light landmarks for mobile devices,” in IPSN-14 proceedings of the 13th international symposium on information processing in sensor networks, pp. 249–260, IEEE, 2014.

  240. F. Schill, U. R. Zimmer, and J. Trumpf, “Visible spectrum optical communication and distance sensing for underwater applications,” in Proceedings of ACRA, pp. 1–8, 2004.

  241. J. M. Kahn and J. R. Barry, “Wireless infrared communications,” Proceedings of the IEEE, vol. 85, no. 2, pp. 265–298, 1997.

    Article  Google Scholar 

  242. C. Sertthin, E. Tsuji, M. Nakagawa, S. Kuwano, and K. Watanabe, “A switching estimated receiver position scheme for visible light based indoor positioning system,” in Wireless Pervasive Computing, 2009. ISWPC 2009. 4th International Symposium on, pp. 1–5, IEEE, 2009.

  243. S.-H. Yang, E.-M. Jung, and S.-K. Han, “Indoor location estimation based on LED visible light communication using multiple optical receivers,” IEEE Communications Letters, vol. 17, no. 9, pp. 1834–1837, 2013.

    Article  Google Scholar 

  244. M. Yasir, S.-W. Ho, and B. N. Vellambi, “Indoor position tracking using multiple optical receivers,” Journal of Lightwave Technology, vol. 34, no. 4, pp. 1166–1176, 2015.

    Article  Google Scholar 

  245. H.-S. Kim, D.-R. Kim, S.-H. Yang, Y.-H. Son, and S.-K. Han, “Inter-cell interference mitigation and indoor positioning system based on carrier allocation visible light communication,” in Signal Processing and Communication Systems (ICSPCS), 2011 5th International Conference on, pp. 1–7, IEEE, 2011.

  246. S.-H. Yang, D.-R. Kim, H.-S. Kim, Y.-H. Son, and S.-K. Han, “Visible light based high accuracy indoor localization using the extinction ratio distributions of light signals,” Microwave and Optical Technology Letters, vol. 55, no. 6, pp. 1385–1389, 2013.

    Article  Google Scholar 

  247. J. Akella, M. Yuksel, and S. Kalyanaraman, “A relative ad hoc localization scheme using optical wireless,” in 2007 2nd International Conference on Communication Systems Software and Middleware, pp. 1–8, IEEE, 2007.

  248. G. Cossu, M. Presi, R. Corsini, P. Choudhury, A. M. Khalid, and E. Ciaramella, “A visible light localization aided optical wireless system,” in 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp. 802–807, IEEE, 2011.

  249. S. Lee and S.-Y. Jung, “Location awareness using angle-of-arrival based circular-PD-array for visible light communication,” in Communications (APCC), 2012 18th Asia-Pacific Conference on, pp. 480–485, IEEE, 2012.

  250. G. B. Prince and T. D. Little, “A two phase hybrid RSS/AOA algorithm for indoor device localization using visible light,” in Global Communications Conference (GLOBECOM), 2012 IEEE, pp. 3347–3352, IEEE, 2012.

  251. S.-H. Yang, H.-S. Kim, Y.-H. Son, and S.-K. Han, “Three-dimensional visible light indoor localization using AOA and RSS with multiple optical receivers,” Journal of Lightwave Technology, vol. 32, no. 14, pp. 2480–2485, 2014.

    Article  Google Scholar 

  252. M. Yoshino, S. Haruyama, and M. Nakagawa, “High-accuracy positioning system using visible LED lights and image sensor,” in Radio and Wireless Symposium, 2008 IEEE, pp. 439–442, IEEE, 2008.

  253. M. S. Rahman, M. M. Haque, and K.-D. Kim, “High precision indoor positioning using lighting LED and image sensor,” in Computer and Information Technology (ICCIT), 2011 14th International Conference on, pp. 309–314, IEEE, 2011.

  254. Y. Nakazawa, H. Makino, K. Nishimori, D. Wakatsuki, and H. Komagata, “Indoor positioning using a high-speed, fish-eye lens-equipped camera in visible light communication,” in Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on, pp. 1–8, IEEE, 2013.

  255. Y.-S. Kuo, P. Pannuto, K.-J. Hsiao, and P. Dutta, “Luxapose: Indoor positioning with mobile phones and visible light,” in Proceedings of the 20th annual international conference on Mobile computing and networking, pp. 447–458, ACM, 2014.

  256. R. Roberts, P. Gopalakrishnan, and S. Rathi, “Visible light positioning: automotive use case,” in Vehicular Networking Conference (VNC), 2010 IEEE, pp. 309–314, IEEE, 2010.

  257. B. Bai, G. Chen, Z. Xu, and Y. Fan, “Visible light positioning based on LED traffic light and photodiode,” in 2011 IEEE Vehicular Technology Conference (VTC Fall), pp. 1–5, IEEE, 2011.

  258. S.-Y. Jung, S. Hann, and C.-S. Park, “TDOA-based optical wireless indoor localization using LED ceiling lamps,” IEEE Transactions on Consumer Electronics, vol. 57, no. 4, 2011.

  259. U. Nadeem, N. Hassan, M. Pasha, and C. Yuen, “Highly accurate 3D wireless indoor positioning system using white LED lights,” Electronics Letters, vol. 50, no. 11, pp. 828–830, 2014.

    Article  Google Scholar 

  260. K. Panta and J. Armstrong, “Indoor localisation using white LEDs,” Electronics letters, vol. 48, no. 4, pp. 228–230, 2012.

    Article  Google Scholar 

  261. Y. Kim, Y. Shin, and M. Yoo, “VLC-TDOA using sinusoidal pilot signal,” in IT Convergence and Security (ICITCS), 2013 International Conference on, pp. 1–3, IEEE, 2013.

  262. T.-H. Do, J. Hwang, and M. Yoo, “TDOA based indoor visible light positioning systems,” in Ubiquitous and Future Networks (ICUFN), 2013 Fifth International Conference on, pp. 456–458, IEEE, 2013.

  263. J. Vongkulbhisal, B. Chantaramolee, Y. Zhao, and W. S. Mohammed, “A fingerprinting-based indoor localization system using intensity modulation of light emitting diodes,” Microwave and Optical Technology Letters, vol. 54, no. 5, pp. 1218–1227, 2012.

    Article  Google Scholar 

  264. A. M. Vegni and M. Biagi, “An indoor localization algorithm in a small-cell LED-based lighting system,” in Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, pp. 1–7, IEEE, 2012.

  265. S. Hann, J.-H. Kim, S.-Y. Jung, and C.-S. Park, “White LED ceiling lights positioning systems for optical wireless indoor applications,” in 36th European Conference and Exhibition on Optical Communication, pp. 1–3, IEEE, 2010.

  266. Y.-Y. Won, S.-H. Yang, D.-H. Kim, and S.-K. Han, “Three-dimensional optical wireless indoor positioning system using location code map based on power distribution of visible light emitting diode,” IET Optoelectronics, vol. 7, no. 3, pp. 77–83, 2013.

    Article  Google Scholar 

  267. M. Liu, K. Qiu, F. Che, S. Li, B. Hussain, L. Wu, and C. P. Yue, “Towards indoor localization using visible light communication for consumer electronic devices,” in Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, pp. 143–148, IEEE, 2014.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seyed (Reza) Zekavat.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zekavat, S., Buehrer, R.M., Durgin, G.D. et al. An Overview on Position Location: Past, Present, Future. Int J Wireless Inf Networks 28, 45–76 (2021). https://doi.org/10.1007/s10776-021-00504-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10776-021-00504-z

Keywords

Navigation