Indoor positioning system

For broader coverage of this topic, see Positioning system.
An indoor location tracking map on a mobile phone

An indoor positioning system (IPS) is a network of devices used to locate people or objects where GPS and other satellite technologies lack precision or fail entirely, such as inside multistory buildings, airports, alleys, parking garages, and underground locations.[1]

A large variety of techniques and devices are used to provide indoor positioning ranging from reconfigured devices already deployed such as smartphones, WiFi and Bluetooth antennas, digital cameras, and clocks; to purpose built installations with relays and beacons strategically placed throughout a defined space. Lights, radio waves, magnetic fields, acoustic signals, and behavioral analytics are all used in IPS networks.[2][3] IPS can achieve position accuracy of 2 cm,[4] which is on par with RTK enabled GNSS receivers that can achieve 2 cm accuracy outdoors.[5] IPS use different technologies, including distance measurement to nearby anchor nodes (nodes with known fixed positions, e.g. WiFi / LiFi access points, Bluetooth beacons or Ultra-Wideband beacons), magnetic positioning, dead reckoning.[6] They either actively locate mobile devices and tags or provide ambient location or environmental context for devices to get sensed.[7][8][9] The localized nature of an IPS has resulted in design fragmentation, with systems making use of various optical,[10] radio,[11][12][13][14][15][16][17] or even acoustic[18][19] technologies.

IPS has broad applications in commercial, military, retail, and inventory tracking industries. There are several commercial systems on the market, but no standards for an IPS system. Instead each installation is tailored to spatial dimensions, building materials, accuracy needs, and budget constraints.

For smoothing to compensate for stochastic (unpredictable) errors there must be a sound method for reducing the error budget significantly. The system might include information from other systems to cope for physical ambiguity and to enable error compensation. Detecting the device's orientation (often referred to as the compass direction in order to disambiguate it from smartphone vertical orientation) can be achieved either by detecting landmarks inside images taken in real time, or by using trilateration with beacons.[20] There also exist technologies for detecting magnetometric information inside buildings or locations with steel structures or in iron ore mines.[21]

  1. ^ Magda Chelly, Nel Samama. New techniques for indoor positioning, combining deterministic and estimation methods. ENC-GNSS 2009 : European Navigation Conference - Global Navigation Satellite Systems, May 2009, Naples, Italy. pp.1 - 12. ⟨hal-01367483⟩ [1]
  2. ^ Curran, Kevin; Furey, Eoghan; Lunney, Tom; Santos, Jose; Woods, Derek; McCaughey, Aiden (2011). "An Evaluation of Indoor Location Determination Technologies". Journal of Location Based Services. 5 (2): 61–78. doi:10.1080/17489725.2011.562927. S2CID 6154778.
  3. ^ Belmonte-Fernández, Ó.; Montoliu, R.; Torres-Sospedra, J.; Sansano-Sansano, E.; Chia-Aguilar, D. (2018). "A radiosity-based method to avoid calibration for indoor positioning systems". Expert Systems with Applications. 105: 89–101. doi:10.1016/j.eswa.2018.03.054. hdl:10234/175947. S2CID 46918367.
  4. ^ "2cm accuracy using an Indoor Positioning System". VBOX Automotive. 2019-11-19. Archived from the original on 2021-01-21. Retrieved 2019-11-19.
  5. ^ "2cm accuracy using RTK". VBOX Automotive. 2019-11-19. Archived from the original on 2021-01-18. Retrieved 2019-11-19.
  6. ^ Qiu, Chen; Mutka, Matt (2016). "CRISP: cooperation among smartphones to improve indoor position information". Wireless Networks. 24 (3): 867–884. doi:10.1007/s11276-016-1373-1. S2CID 3941741.
  7. ^ Furey, Eoghan; Curran, Kevin; McKevitt, Paul (2012). "HABITS: A Bayesian Filter Approach to Indoor Tracking and Location". International Journal of Bio-Inspired Computation. 4 (2): 79. CiteSeerX 10.1.1.459.8761. doi:10.1504/IJBIC.2012.047178.
  8. ^ THES, Propagation of a position in a connected network, Chelly, Magda Lilia; Samama, Nel; 2011, 2011TELE0018, [2]
  9. ^ Yasir, M.; Ho, S.; Vellambi, B. N. (2014). "Indoor Positioning System Using Visible Light and Accelerometer". Journal of Lightwave Technology. 32 (19): 3306–3316. Bibcode:2014JLwT...32.3306Y. doi:10.1109/jlt.2014.2344772. S2CID 25188925.
  10. ^ Liu X, Makino H, Mase K. 2010. Improved indoor location estimation using fluorescent light communication system with a nine-channel receiver. IEICE Transactions on Communications E93-B(11):2936-44.
  11. ^ Farid, Z.; Nordin, R.; Ismail, M. (2013). "Recent Advances in Wireless Indoor Localization Techniques and System". Journal of Computer Networks and Communications. 2013: 1–12. doi:10.1155/2013/185138.
  12. ^ Chang, N; Rashidzadeh, R; Ahmadi, M (2010). "Robust indoor positioning using differential Wi-Fi access points". IEEE Transactions on Consumer Electronics. 56 (3): 1860–7. doi:10.1109/tce.2010.5606338. S2CID 37179475.
  13. ^ Atia, M. M.; Noureldin, A.; Korenberg, M. J. (2013). "Dynamic Online-Calibrated Radio Maps for Indoor Positioning in Wireless Local Area Networks". IEEE Transactions on Mobile Computing. 12 (9): 1774–1787. doi:10.1109/tmc.2012.143. S2CID 15669485.
  14. ^ Chiou, Y; Wang, C; Yeh, S (2010). "An adaptive location estimator using tracking algorithms for indoor WLANs". Wireless Networks. 16 (7): 1987–2012. doi:10.1007/s11276-010-0240-8. S2CID 41494773.
  15. ^ Lim, H; Kung, L; Hou, JC; Haiyun, Luo (2010). "Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure". Wireless Networks. 16 (2): 405–20. doi:10.1007/s11276-008-0140-3. S2CID 17678327.
  16. ^ Reza, AW; Geok, TK (2009). "Investigation of indoor location sensing via RFID reader network utilizing grid covering algorithm". Wireless Personal Communications. 49 (1): 67–80. doi:10.1007/s11277-008-9556-4. S2CID 5562161.
  17. ^ Zhou, Y; Law, CL; Guan, YL; Chin, F (2011). "Indoor elliptical localization based on asynchronous UWB range measurement". IEEE Transactions on Instrumentation and Measurement. 60 (1): 248–57. Bibcode:2011ITIM...60..248Z. doi:10.1109/tim.2010.2049185. S2CID 12880695.
  18. ^ Schweinzer, H; Kaniak, G (2010). "Ultrasonic device localization and its potential for wireless sensor network security". Control Engineering Practice. 18 (8): 852–62. doi:10.1016/j.conengprac.2008.12.007.
  19. ^ Qiu, Chen; Mutka, Matt (2017). "Silent whistle: Effective indoor positioning with assistance from acoustic sensing on smartphones". 2017 IEEE 18th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). pp. 1–6. doi:10.1109/WoWMoM.2017.7974312. ISBN 978-1-5386-2723-5. S2CID 30783515.
  20. ^ Positioning and orientation using image processing a 2007 research from the University of Washington. Several similar approaches have been developed and there are currently (2017) smartphone applications implementing this technology.
  21. ^ Startup uses a smartphone to track people indoors, - About Indoor Atlass (MIT Technology Review website)