indoor positioning system

Due to the signal attenuation caused by construction materials, the satellite based Global Positioning System (GPS) loses significant power indoors affecting the required coverage for receivers by at least four satellites. In addition, the multiple reflections at surfaces cause multi-path propagation serving for uncontrollable errors. These very same effects are degrading all known solutions for indoor locating which uses electromagnetic waves from indoor transmitters to indoor receivers. A bundle of physical and mathematical methods are applied to compensate for these problems. Promising direction radio frequency positioning error correction opened by the use of alternative sources of navigational information, such as inertial measurement unit (IMU), monocular camera Simultaneous localization and mapping (SLAM) and WiFi SLAM. Integration of data from various navigation systems with different physical principles can increase the accuracy and robustness of the overall solution.Vladimir Maximov and Oleg Tabarovsky, LLC RTLS, Moscow, Russia (2013). Survey of Accuracy Improvement Approaches for Tightly Coupled ToA/IMU Personal Indoor Navigation System. Proceedings of International Conference on Indoor Positioning and Indoor Navigation, October 2013, Montbeliard, France.[http://community.sk.ru/cfs-filesystemfile.ashx/__key/telligent-evolution-components-attachments/01-6034-00-00-00-03-53-32/maximov_5F00_tabarovsky_5F00_updated_5F00_.pdf See publication here] {{Webarchive|url=https://web.archive.org/web/20141205154609/http://community.sk.ru/cfs-filesystemfile.ashx/__key/telligent-evolution-components-attachments/01-6034-00-00-00-03-53-32/maximov_5F00_tabarovsky_5F00_updated_5F00_.pdf |date=2014-12-05 }}

The U.S. Global Positioning System (GPS) and other similar global navigation satellite systems (GNSS) are generally not suitable to establish indoor locations, since microwaves will be attenuated and scattered by roofs, walls and other objects. However, in order to make the positioning signals become ubiquitous, integration between GPS and indoor positioning can be made.Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad and Raja Zahilah (2015). Offline Beacon Selection-Based RSSI Fingerprinting for Location-Aware Shopping Assistance: A Preliminary Result. New Trends in Intelligent Information and Database Systems, pp. 303-312, [https://link.springer.com/chapter/10.1007%2F978-3-319-16211-9_31 See publication here]Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad and Raja Zahilah (2015). Emergency Rescue Localization (ERL) using GPS, Wireless LAN and Camera" International Journal of Software Engineering and Its Applications, Vol. 9, No. 9, pp. 217-232, https://serscjournals.org/index.php/IJSEIA/vol9_no9_2015/19.pdf{{Dead link|date=March 2023 |bot=InternetArchiveBot |fix-attempted=yes }}Wan Mohd Yaakob Wan Bejuri and Mohd Murtadha Mohamad (2014). Performance Analysis of Grey-World-based Feature Detection and Matching for Mobile Positioning Systems. Sensing and Imaging, Vol. 15, No. 1, pp. 1-24 [https://link.springer.com/article/10.1007/s11220-014-0095-7]{{cite journal | last1 = Wan Mohd | first1 = Yaakob Wan Bejuri | last2 = Murtadha Mohamad | first2 = Mohd | year = 2014 | title = Wireless LAN/FM Radio-based Robust Mobile Indoor Positioning: An Initial Outcome | url = https://serscjournals.org/index.php/IJSEIA/vol8_no2_2014/31.pdf | archive-url = https://web.archive.org/web/20231030063212/https://serscjournals.org/index.php/IJSEIA/vol8_no2_2014/31.pdf | url-status = dead | archive-date = October 30, 2023 | journal = International Journal of Software Engineering and Its Applications | volume = 8 | issue = 2 | pages = 313–324 }}{{cite journal | last1 = Wan | last2 = Yaakob Wan Bejuri | first2 = Mohd | last3 = Murtadha Mohamad | first3 = Mohd | last4 = Sapri | first4 = Maimunah | last5 = Shafry Mohd Rahim | first5 = Mohd | last6 = Ahsenali Chaudry | first6 = Junaid | year = 2014 | title = Performance Evaluation of Spatial Correlation-based Feature Detection and Matching for Automated Wheelchair Navigation System | journal = International Journal of Intelligent Transportation Systems Research | volume = 12 | issue = 1| pages = 9–19 | doi=10.1007/s13177-013-0064-x| s2cid = 3478714 }}Wan Mohd Yaakob Wan Bejuri, Wan Mohd Nasri Wan Muhamad Saidin, Mohd Murtadha Mohamad, Maimunah Sapri and Kah Seng Lim (2013). Ubiquitous Positioning: Integrated GPS/Wireless LAN Positioning for Wheelchair Navigation System. Intelligent Information and Database Systems, Vol. 7802, pp. 394-403, [https://link.springer.com/chapter/10.1007/978-3-642-36546-1_41#page-1 See publication here]Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Maimunah Sapri and Mohd Adly Rosly (2012). Ubiquitous WLAN/Camera Positioning using Inverse Intensity Chromaticity Space-based Feature Detection and Matching: A Preliminary Result. International Conference on Man-Machine Systems 2012 (ICOMMS 2012). [https://arxiv.org/abs/1204.2294 See publication here]Z. Horvath, H. Horvath (2014): The Measurement Preciseness of the GPS Built in Smartphones and Tablets, International Journal on Electronics and Communication Technology, issue 1, pp 17-19, [http://www.iject.org/vol5/spl1/zoltan.pdf]

Currently, GNSS receivers are becoming more and more sensitive due to increasing microchip processing power. High sensitivity GNSS receivers are able to receive satellite signals in most indoor environments and attempts to determine the 3D position indoors have been successful.{{cite web|url=http://www.insidegnss.com/node/590|title=GNSS Indoors — Fighting The Fading, Part 1 - Inside GNSS|website=www.insidegnss.com|date=2008-03-12|access-date=2009-10-18|archive-url=https://web.archive.org/web/20180110160720/http://www.insidegnss.com/node/590|archive-date=2018-01-10|url-status=dead}} Besides increasing the sensitivity of the receivers, the technique of A-GPS is used, where the almanac and other information are transferred through a mobile phone.

However, despite the fact that proper coverage for the required four satellites to locate a receiver is not achieved with all current designs (2008–11) for indoor operations, GPS emulation has been deployed successfully in Stockholm metro.{{Cite web|url=https://www.gpsworld.com/syntony-rises-high-by-going-underground/#:~:text=Syntony%20GNSS%2C%20a%20simulator%20company,sea%20in%20and%20around%20Stockholm.|title = Syntony rises high by going underground|date = November 2016}} GPS coverage extension solutions have been able to provide zone-based positioning indoors, accessible with standard GPS chipsets like the ones used in smartphones.

While most current IPS are able to detect the location of an object, they are so coarse that they cannot be used to detect the orientation or direction of an object.{{cite journal | last1 = Furey | first1 = Eoghan | last2 = Curran | first2 = Kevin | last3 = McKevitt | first3 = Paul | year = 2012 | title = Probabilistic Indoor Human Movement Modeling to Aid First Responders | journal = Journal of Ambient Intelligence and Humanized Computing | volume = 3 | issue = 5| pages = 559–569| doi = 10.1007/s12652-012-0112-4 | s2cid = 16611408 | url = https://pure.ulster.ac.uk/en/publications/77418e4b-bba7-4e59-9b48-022512f581f3 }}

{{See also|Waypoint|point of interest}}

One of the methods to thrive for sufficient operational suitability is "tracking". Whether a sequence of locations determined form a trajectory from the first to the most actual location. Statistical methods then serve for smoothing the locations determined in a track resembling the physical capabilities of the object to move. This smoothing must be applied, when a target moves and also for a resident target, to compensate erratic measures. Otherwise the single resident location or even the followed trajectory would compose of an itinerant sequence of jumps.

In most applications the population of targets is larger than just one. Hence the IPS must serve a proper specific identification for each observed target and must be capable to segregate and separate the targets individually within the group. An IPS must be able to identify the entities being tracked, despite the "non-interesting" neighbors. Depending on the design, either a sensor network must know from which tag it has received information, or a locating device must be able to identify the targets directly.

Any wireless technology can be used for locating. Many different systems take advantage of existing wireless infrastructure for indoor positioning. There are three primary system topology options for hardware and software configuration, network-based, terminal-based, and terminal-assisted. Positioning accuracy can be increased at the expense of wireless infrastructure equipment and installations.

{{Main|Wi-Fi positioning system}}

{{Further|Wi-Fi Round Trip Time}}

Wi-Fi positioning system (WPS) is used where GPS is inadequate. The localization technique used for positioning with wireless access points is based on measuring the intensity of the received signal (received signal strength in English RSS) and the method of "fingerprinting".{{Cite book|last1=Violettas|first1=G. E.|last2=Theodorou|first2=T. L.|last3=Georgiadis|first3=C. K.|date=August 2009 |doi=10.1109/ICWMC.2009.64|isbn=978-1-4244-4679-7|chapter=Net Argus: An SNMP Monitor & Wi-Fi Positioning, 3-tier Application Suite|title=2009 Fifth International Conference on Wireless and Mobile Communications|pages=346–351|s2cid=23482772}}P. Bahl and V. N. Padmanabhan, "[https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/infocom2000.pdf RADAR: an in-building RF-based user location and tracking system]," in Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00), vol. 2, pp. 775–784, Tel Aviv.Israel, March 2000.{{Cite journal|last1=Youssef|first1=Moustafa|last2=Agrawala|first2=Ashok|date=2007-01-04|title=The Horus location determination system|journal=Wireless Networks|volume=14|issue=3|pages=357–374|doi=10.1007/s11276-006-0725-7|s2cid=62768948|issn=1022-0038}}Y. Chen and H. Kobayashi, "[https://web.archive.org/web/20190406183141/https://pdfs.semanticscholar.org/8d86/88ea6927b6931057bcb7a79a43a0c8596361.pdf Signal strength based indoor geolocation]," in Proceedings of the IEEE International Conference on Communications (ICC '02), vol. 1, pp. 436–439, New York, NY, USA, April–May 2002. To increase the accuracy of fingerprinting methods, statistical post-processing techniques (like Gaussian process theory) can be applied, to transform discrete set of "fingerprints" to a continuous distribution of RSSI of each access point over entire location.Golovan A. A. et al. Efficient localization using different mean offset models in Gaussian processes //2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN). – IEEE, 2014. – С. 365-374.[https://ieeexplore.ieee.org/abstract/document/7275504]Hähnel B. F. D., Fox D. Gaussian processes for signal strength-based location estimation //Proceeding of robotics: science and systems. – 2006.[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.143.5118&rep=rep1&type=pdf]Ferris B., Fox D., Lawrence N. D. Wifi-slam using gaussian process latent variable models //IJCAI. – 2007. – Т. 7. – №. 1. – С. 2480-2485.[https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-399.pdf]{{Webarchive|url=https://web.archive.org/web/20221224110401/https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-399.pdf|date=2022-12-24}} Typical parameters useful to geolocate the Wi-Fi hotspot or wireless access point include the SSID and the MAC address of the access point. The accuracy depends on the number of positions that have been entered into the database. The possible signal fluctuations that may occur can increase errors and inaccuracies in the path of the user.{{cite book | doi = 10.1145/2737095.2737726 |pages=178–189| year=2015 | last1 = Lymberopoulos | first1 = Dimitrios | last2 = Liu | first2 = Jie | last3 = Yang | first3 = Xue | last4 = Roy Choudhury | first4 = Romit | last5 = Handziski | first5 = Vlado | last6 = Sen | first6 = Souvik|title=Proceedings of the 14th International Conference on Information Processing in Sensor Networks |chapter=A realistic evaluation and comparison of indoor location technologies |isbn=9781450334754|s2cid=1028754}}{{cite book | last1 = Laoudias | first1 = C. | title = 2012 IEEE 13th International Conference on Mobile Data Management | pages = 312–315 | last2 = Constantinou | first2 = G. | last3 = Constantinides | first3 = M. | last4 = Nicolaou | first4 = S. | last5 = Zeinalipour-Yazti | first5 = D. | last6 = Panayiotou | first6 = C. G. | year = 2012 | doi = 10.1109/MDM.2012.68 | isbn = 978-1-4673-1796-2 | chapter = The Airplace Indoor Positioning Platform for Android Smartphones | s2cid = 14903792 }} (Best Demo Award)

Originally, Bluetooth was concerned about proximity, not about exact location.{{cite web|url=https://www.brighttalk.com/webcast/635/107617 |title=Everything You Always Wanted To Know about Beacons |publisher=Bright Talk |access-date=2014-06-12}}

Bluetooth was not intended to offer a pinned location like GPS, however is known as a geo-fence or micro-fence solution which makes it an indoor proximity solution, not an indoor positioning solution.

Micromapping and indoor mapping{{cite web|url=http://www.businessinsider.com/apple-indoor-mapping-project-and-ibeacon-2014-6 |title=Apple Is Launching A Vast Project To Map The Inside Of Every Large Building It Can |website=Business Insider |access-date=2014-06-12}} has been linked to Bluetooth{{cite web|url=http://www.valuewalk.com/2014/01/apple-inc-aapl-ibeacon-with-micromapping/|title=Apple Inc. iBeacon with Micromapping can revolutionize retail |publisher=ValueWalk|date= January 2014|access-date=2014-06-12}} and to the Bluetooth LE based iBeacon promoted by Apple Inc. Large-scale indoor positioning system based on iBeacons has been implemented and applied in practice.{{cite web|url=http://www.nashville.gov/News-Media/News-Article/ID/3477/Mayor-Music-City-Center-Unveil-Wayfinding-App|title=Music City Center Unveil Wayfinding App|access-date=2014-11-28|archive-date=2014-12-05|archive-url=https://web.archive.org/web/20141205172800/http://www.nashville.gov/News-Media/News-Article/ID/3477/Mayor-Music-City-Center-Unveil-Wayfinding-App|url-status=dead}}{{cite web|url=http://www.tennessean.com/story/money/tech/2014/11/12/music-city-center-app-guides-visitors/18945489/|title=Music City Center app guides visitors|website=The Tennessean |access-date=2014-11-28}}

Bluetooth speaker position and home networks can be used for broad reference.

In 2021 Apple released their AirTags which allow a combination of Bluetooth and UWB technology to track Apple devices amongst the Find My network causing a surge of popularity for tracking technology.

Simple concept of location indexing and presence reporting for tagged objects, uses known sensor identification only. This is usually the case with passive radio-frequency identification (RFID) / NFC systems, which do not report the signal strengths and various distances of single tags or of a bulk of tags and do not renew any before known location coordinates of the sensor or current location of any tags. Operability of such approaches requires some narrow passage to prevent from passing by out of range.

Instead of long range measurement, a dense network of low-range receivers may be arranged, e.g. in a grid pattern for economy, throughout the space being observed. Due to the low range, a tagged entity will be identified by only a few close, networked receivers. An identified tag must be within range of the identifying reader, allowing a rough approximation of the tag location. Advanced systems combine visual coverage with a camera grid with the wireless coverage for the rough location.

Most systems use a continuous physical measurement (such as angle and distance or distance only) along with the identification data in one combined signal. Reach by these sensors mostly covers an entire floor, or an aisle or just a single room. Short reach solutions get applied with multiple sensors and overlapping reach.

Angle of arrival (AoA) is the angle from which a signal arrives at a receiver. AoA is usually determined by measuring the time difference of arrival (TDOA) between multiple antennas in a sensor array. In other receivers, it is determined by an array of highly directional sensors—the angle can be determined by which sensor received the signal. AoA is usually used with triangulation and a known base line to find the location relative to two anchor transmitters.

Time of arrival (ToA, also time of flight) is the amount of time a signal takes to propagate from transmitter to receiver. Because the signal propagation rate is constant and known (ignoring differences in mediums) the travel time of a signal can be used to directly calculate distance. Multiple measurements can be combined with trilateration and multilateration to find a location. This is the technique used by GPS and Ultra Wideband systems. Systems which use ToA, generally require a complicated synchronization mechanism to maintain a reliable source of time for sensors (though this can be avoided in carefully designed systems by using repeaters to establish coupling).

The accuracy of the TOA based methods often suffers from massive multipath conditions in indoor localization, which is caused by the reflection and diffraction of the RF signal from objects (e.g., interior wall, doors or furniture) in the environment. However, it is possible to reduce the effect of multipath by applying temporal or spatial sparsity based techniques.{{cite journal

| last1=Pourhomayoun

| last2=Jin

| last3=Fowler

| year=2012

| title=Spatial Sparsity Based Indoor Localization in Wireless Sensor Network for Assistive Healthcare Systems

| journal=Embc2012|url=http://ws2.binghamton.edu/fowler/Spatial%20Sparsity%20Based%20Indoor%20Localization%20in%20Wireless%20Sensor%20Network%20for%20Assistive%20Healthcare%20Systems%20-%20Rev2.pdf}}

C.R. Comsa, et al.,“[https://www.researchgate.net/profile/Ciprian_Comsa/publication/220734570_Source_localization_using_time_difference_of_arrival_within_a_sparse_representation_framework/links/0a85e531c937c0bda3000000/Source-localization-using-time-difference-of-arrival-within-a-sparse-representation-framework.pdf Source Localization Using Time Difference Of Arrival Within A Sparse Representation Framework]”, ICASSP, 2011.

Joint estimation of angles and times of arrival is another method of estimating the location of the user. Indeed, instead of requiring multiple access points and techniques such as triangulation and trilateration, a single access point will be able to locate a user with combined angles and times of arrival.Wen, Fuxi, and Chen Liang. "Fine-grained indoor localization using single access point with multiple antennas." IEEE Sensors Journal 15.3 (2014): 1538-1544. Even more, techniques that leverage both space and time dimensions can increase the degrees of freedom of the whole system and further create more virtual resources to resolve more sources, via subspace approaches.Ahmad Bazzi, Dirk TM Slock, and Lisa Meilhac. "Single snapshot joint estimation of angles and times of arrival: A 2D Matrix Pencil approach." 2016 IEEE International Conference on Communications (ICC). IEEE, 2016.

Received signal strength indication (RSSI) is a measurement of the power level received by sensor. Because radio waves propagate according to the inverse-square law, distance can be approximated (typically to within 1.5 meters in ideal conditions and 2 to 4 meters in standard conditions{{Cite book|last1=Zhan Jie|last2=Liu HongLi|last3=Tanjian|title=The 2nd International Conference on Information Science and Engineering |chapter=Research on ranging accuracy based on RSSI of wireless sensor network |date=December 2010|pages=2338–2341|doi=10.1109/ICISE.2010.5691135|isbn=978-1-4244-7616-9|s2cid=14465473}}) based on the relationship between transmitted and received signal strength (the transmission strength is a constant based on the equipment being used), as long as no other errors contribute to faulty results. The inside of buildings is not free space, so accuracy is significantly impacted by reflection and absorption from walls. Non-stationary objects such as doors, furniture, and people can pose an even greater problem, as they can affect the signal strength in dynamic, unpredictable ways.

A lot of systems use enhanced Wi-Fi infrastructure to provide location information. None of these systems serves for proper operation with any infrastructure as is. Unfortunately, Wi-Fi signal strength measurements are extremely noisy, so there is ongoing research focused on making more accurate systems

• Radio frequency identification (RFID): passive tags are very cost-effective, but do not support any metrics
• Ultra-wideband{{Cite web | url=https://www.gpsworld.com/racelogic-introduces-vbox-indoor-positioning-system-at-ion-gnss/ |title = Racelogic debuts VBOX indoor positioning system|date = 2018-09-24}} (UWB): reduced interference with other devices
• Infrared (IR): previously included in most mobile devices
• Gen2IR (second generation infrared)
• Visible light communicationLee, Yong Up; Kavehrad, Mohsen;, "Long-range indoor hybrid localization system design with visible light communications and wireless network," Photonics Society Summer Topical Meeting Series, 2012 IEEE, vol., no., pp.82-83, 9–11 July 2012 [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6280712 See publication here] (VLC), as LiFi: can use existing lighting systems
• Ultrasound: waves move very slowly, which results in much higher accuracy

Non-radio technologies can be used for positioning without using the existing wireless infrastructure. This can provide increased accuracy at the expense of costly equipment and installations.

{{Main|Magnetic positioning}}

Magnetic positioning can offer pedestrians with smartphones an indoor accuracy of 1–2 meters with 90% confidence level, without using the additional wireless infrastructure for positioning. Magnetic positioning is based on the iron inside buildings that create local variations in the Earth's magnetic field. Un-optimized compass chips inside smartphones can sense and record these magnetic variations to map indoor locations.{{cite magazine|url=http://www.geospatialworld.net/wp-content/uploads/magazine/Geospatial-World-August-2014.pdf|magazine=Geospatial World|title=Geospatial World August 2014}}

Pedestrian dead reckoning and other approaches for positioning of pedestrians propose an inertial measurement unit carried by the pedestrian either by measuring steps indirectly (step counting) or in a foot mounted approach,{{cite journal|title=Pedestrian Tracking with Shoe-Mounted Inertial Sensors|first=Eric|last=Foxlin|date=1 November 2005|journal=IEEE Computer Graphics and Applications|volume = 25|issue=6|pages=38–46|doi=10.1109/MCG.2005.140|pmid=16315476|s2cid=19038276}} sometimes referring to maps or other additional sensors to constrain the inherent sensor drift encountered with inertial navigation. The MEMS inertial sensors suffer from internal noises which result in cubically growing position error with time. To reduce the error growth in such devices a Kalman Filtering based approach is often used.{{Cite book |doi=10.1109/IPIN.2017.8115944 |isbn=978-1-5090-6299-7|chapter=On the noise and power performance of a shoe-mounted multi-IMU inertial positioning system|title=2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)|pages=1–8|year=2017|last1=Bose|first1=Subhojyoti|last2=Gupta|first2=Amit K.|last3=Handel|first3=Peter|s2cid=19055090}}{{Cite book |doi=10.1109/INDICON.2015.7443478|isbn=978-1-4673-7399-9|chapter=Long-term performance evaluation of a foot-mounted pedestrian navigation device|title=2015 Annual IEEE India Conference (INDICON)|pages=1–6|year=2015|last1=Gupta|first1=Amit K.|last2=Skog|first2=Isaac|last3=Handel|first3=Peter|s2cid=33398667}}{{Cite book |doi=10.1109/IPIN.2014.7275464|isbn=978-1-4673-8054-6|chapter=Foot-mounted inertial navigation made easy|title=2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN)|pages=24–29|year=2014|last1=Nilsson|first1=John-Olof|last2=Gupta|first2=Amit K.|last3=Handel|first3=Peter|s2cid=898076}}{{Cite book |doi=10.1109/IPIN.2017.8115916|isbn=978-1-5090-6299-7|chapter=A foot-mounted PDR system based on IMU/EKF+HMM+ZUPT+ZARU+HDR+compass algorithm|title=2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)|pages=1–5|year=2017|last1=Zhang|first1=Wenchao|last2=Li|first2=Xianghong|last3=Wei|first3=Dongyan|last4=Ji|first4=Xinchun|last5=Yuan|first5=Hong|s2cid=19693291}}

However, in order to make it capable to build map itself, the SLAM algorithm framework Simultaneous localization and mapping will be used.Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Raja Zahilah (2015). A Proposal of Emergency Rescue Location (ERL) using Optimization of Inertial Measurement Unit (IMU) based Pedestrian Simultaneously Localization and Mapping (SLAM). International Journal of Smart Home. Vol 9: No.12, pp: 9-22.https://serscjournals.org/index.php/IJSH/vol9_no12_2015/2.pdfWan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Raja Zahilah (2015). Optimisation of Emergency Rescue Location (ERL) using KLD Resampling: An Initial Proposal. International Journal of u- and e- Service, Science and Technology. Vol 9: No.2, pp: 249-262. https://serscjournals.org/index.php/IJUNESST/vol9_no2/25.pdf{{Dead link|date=March 2023 |bot=InternetArchiveBot |fix-attempted=yes }}

Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Raja Zahilah (2015). Optimization of Rao-Blackwellized Particle Filter in Activity Pedestrian Simultaneously Localization and Mapping (SLAM): An Initial Proposal. International Journal of Security and Its Applications. Vol 9: No.11, pp: 377-390. https://serscjournals.org/index.php/IJSIA/vol9_no11_2015/35.pdf{{Dead link|date=March 2023 |bot=InternetArchiveBot |fix-attempted=yes }}

Inertial measures generally cover the differentials of motion, hence the location gets determined with integrating and thus requires integration constants to provide results.{{cite web|url=http://www.kn-s.dlr.de/indoornav/|title=Sensor fusion and map aiding for indoor navigation|url-status=dead|archive-url=https://web.archive.org/web/20100428011516/http://www.kn-s.dlr.de/indoornav/|archive-date=2010-04-28}}{{cite web|url=http://www.cl.cam.ac.uk/research/dtg/www/publications/public/ojw28/Main-PersonalRedist.pdf|title=Pedestrian localization for indoor environments|access-date=2012-06-25|archive-date=2017-07-05|archive-url=https://web.archive.org/web/20170705142629/http://www.cl.cam.ac.uk/research/dtg/www/publications/public/ojw28/Main-PersonalRedist.pdf|url-status=dead}} The actual position estimation can be found as the maximum of a 2-d probability distribution which is recomputed at each step taking into account the noise model of all the sensors involved and the constraints posed by walls and furniture.{{cite journal|doi=10.1080/17489725.2015.1027751 | volume=9 | title=Infrastructure-free indoor navigation: a case study | year=2015 | journal=Journal of Location Based Services | pages=33–54 | last1 = Carboni | first1 = Davide | last2 = Manchinu | first2 = Andrea | last3 = Marotto | first3 = Valentina | last4 = Piras | first4 = Andrea | last5 = Serra | first5 = Alberto| s2cid=34080648 }}

Based on the motions and users' walking behaviors, IPS is able to estimate users' locations by machine learning algorithms.{{cite book | last1 = Qiu | first1 = Chen | title = 2017 IEEE 18th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) | pages = 1–9 | last2 = Mutka | first2 = Matt| year = 2017 | doi=10.1109/WoWMoM.2017.7974311| isbn = 978-1-5386-2723-5 | chapter = Self-improving indoor localization by profiling outdoor movement on smartphones | s2cid = 8560911 }}

A visual positioning system can determine the location of a camera-enabled mobile device by decoding location coordinates from visual markers. In such a system, markers are placed at specific locations throughout a venue, each marker encoding that location's coordinates: latitude, longitude and height off the floor. Measuring the visual angle from the device to the marker enables the device to estimate its own location coordinates in reference to the marker. Coordinates include latitude, longitude, level and altitude off the floor.{{cite web|url= https://www.mmh.com/article/information_management_wearables_come_in_for_a_refit | title=Wearables come in for a refit}}{{Cite journal|last1=Daniş|first1=F. Serhan|last2=Naskali|first2=A. Teoman|last3=Cemgil|first3=A. Taylan|last4=Ersoy|first4=Cem|date=2022|title=An indoor localization dataset and data collection framework with high precision position annotation|journal=Pervasive and Mobile Computing|volume=81|pages=101554|doi=10.1016/j.pmcj.2022.101554|s2cid=246887116 |arxiv=2209.02270}} As visual markers usually are not symmetric, also the orientation of the user can be determined.{{Cite book |last1=Wortmann |first1=Johannes |last2=Schäufele |first2=Bernd |last3=Klipp |first3=Konstantin |last4=Radusch |first4=Ilja |last5=Blaß |first5=Katharina |last6=Jung |first6=Thomas |chapter=Enhanced Accessibility for Mobile Indoor Navigation |date=October 2024 |title=2024 14th International Conference on Indoor Positioning and Indoor Navigation (IPIN) |chapter-url=https://ieeexplore.ieee.org/document/10786147/;jsessionid=09EB88D71CD08765B965DAB44DB2F6B1 |pages=1–6 |doi=10.1109/IPIN62893.2024.10786147|isbn=979-8-3503-6640-2 }}

A collection of successive snapshots from a mobile device's camera can build a database of images that is suitable for estimating location in a venue. Once the database is built, a mobile device moving through the venue can take snapshots that can be interpolated into the venue's database, yielding location coordinates. These coordinates can be used in conjunction with other location techniques for higher accuracy. Note that this can be a special case of sensor fusion where a camera plays the role of yet another sensor.

Once sensor data has been collected, an IPS tries to determine the location from which the received transmission was most likely collected. The data from a single sensor is generally ambiguous and must be resolved by a series of statistical procedures to combine several sensor input streams.

One way to determine position is to match the data from the unknown location with a large set of known locations using an algorithm such as k-nearest neighbor. This technique requires a comprehensive on-site survey and will be inaccurate with any significant change in the environment (due to moving persons or moved objects).

Location will be calculated mathematically by approximating signal propagation and finding angles and / or distance. Inverse trigonometry will then be used to determine location:

• Trilateration (distance from anchors)
• Triangulation (angle to anchors)

Advanced systems combine more accurate physical models with statistical procedures:

• Bayesian statistical analysis (probabilistic model) {{Cite journal|last1=Al-Ahmadi|first1=Abdullah|last2=Qasaymeh|first2=Yazeed Mohammad|last3=R. P.|first3=Praveen|last4=Alghamdi|first4=Ali|date=2019|journal=Progress in Electromagnetics Research M|volume=83|pages=41–50|doi=10.2528/pierm19042804|issn=1937-8726|title=Bayesian Approach for Indoor Wave Propagation Modeling|doi-access=free}}
• Kalman filtering (for estimating proper value streams under noise conditions).
• Sequential Monte Carlo method (for approximating the Bayesian statistical models).{{Cite journal|last1=Daniş|first1=F. Serhan|last2=Cemgil|first2=A. Taylan|last3=Ersoy|first3=Cem|date=2021|title=Adaptive Sequential Monte Carlo Filter for Indoor Positioning and Tracking with Bluetooth Low Energy Beacons|journal=IEEE Access|volume=9|pages=37022–37038|doi=10.1109/ACCESS.2021.3062818|bibcode=2021IEEEA...937022D |doi-access=free}}

The major consumer benefit of indoor positioning is the expansion of location-aware mobile computing indoors. As mobile devices become ubiquitous, contextual awareness for applications has become a priority for developers. Most applications currently rely on GPS, however, and function poorly indoors. Applications benefiting from indoor location include:

• Accessibility aids for the visually impaired.{{cite conference|pmc=4512241|year=2014|last1=Bai|first1=Y|conference=12th International Conference on Signal Processing (ICSP)|volume=2014|pages=678–681|last2=Jia|first2=W|last3=Zhang|first3=H|last4=Mao|first4=Z. H.|last5=Sun|first5=M|doi=10.1109/ICOSP.2014.7015087|pmid=26213718|title=Landmark-based indoor positioning for visually impaired individuals|isbn=978-1-4799-2186-7}}
• Augmented reality{{cite web | url=http://arabcrunch.com/2010/03/junaio-2-0-first-indoor-social-augmented-reality-app-at-sxsw-with-developers-api.html | title=Junaio 2.0 First Indoor Social Augmented Reality App at SXSW With Developers API | date=March 2010 | author=Gaith Saqer | url-status=usurped | archive-url=https://web.archive.org/web/20100312133449/http://arabcrunch.com/2010/03/junaio-2-0-first-indoor-social-augmented-reality-app-at-sxsw-with-developers-api.html | archive-date=2010-03-12}}
• School campus
• Museum guided tours{{cite web|url=https://www.engadget.com/2010/12/13/fraunhofer-iis-uses-awiloc-indoor-positioning-magic-to-guide-mus/|title=Fraunhofer IIS uses Awiloc indoor positioning magic to guide museum patrons|date=13 December 2010 }}
• Shopping malls, including hypermarkets.
• Warehouses
• Factory
• Airports, bus, train and subway stations
• Parking lots, including these in hypermarkets
• Targeted advertising
• Social networking service
• Hospitals
• Hotels
• Sports
• Cruise ships
• Indoor robotics{{Cite book|last1=Qiu|first1=C.|last2=Mutka|first2=M. W.|title=2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems |chapter=AirLoc: Mobile Robots Assisted Indoor Localization |date=2015-10-01|pages=407–415|doi=10.1109/MASS.2015.10|isbn=978-1-4673-9101-6|s2cid=13133026}}
• Tourism
• Amusement parks
• [https://navigosmarttech.co.uk/unveiling-the-intricacies-of-indoor-data-modeling-for-enhanced-navigation/ Indoor data modeling]

{{Columns-list|colwidth=25em|

• Automatic vehicle location
• Bluetooth Low Energy
• Cyber-physical system
• Dead reckoning
• Earth's magnetic field
• Floor plans and house navigation system.
• Geolocation
• Google Indoor Maps
• GSM localization
• Home automation
• Home network
• Internet of things (IoT)
• Location-based service
• Motion planning
• Navizon
• Near field communication (NFC)
• Omlox
• Open Source Routing Machine
• Pointr
• Real-time locating system (RTLS)
• Simultaneous localization and mapping (SLAM).
• Robotic mapping
• Sensor Fusion
• Skyhook Wireless
• Visible light communication (VLC) and positioning and Li-Fi
• Wayfinding
• Wayfinding (urban or indoor)
• WebGL

}}

{{Reflist|30em}}

• [https://www.identecsolutions.com/asset-agent-for-smart-factory Asset Agent] Indoor RTLS, based on active RFID and Chirp technology
• [https://www.pozyx.io/ Pozyx] Indoor Real-Time Location System (RTLS), based on UWB technology
• [https://openhps.org OpenHPS] Hybrid Solution for Indoor and Outdoor Positioning
• [https://wiki.openstreetmap.org/wiki/Micromapping Micromapping] in OpenStreetMap
• [https://wiki.openstreetmap.org/wiki/Indoor_Mapping Indoor Mapping] in OpenStreetMap
• [http://www.ipin-conference.org/ IPIN Conferences.]

{{Positional tracking}}

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{{DEFAULTSORT:Indoor Positioning System}}

Category:Automatic identification and data capture

Category:Radio-frequency identification

Category:Radio navigation

Category:Ubiquitous computing

Category:Maps

Category:Geomagnetism

Category:Geopositioning