Indoor positioning system
An indoor positioning system (IPS) or micromapping is a network of devices used to wirelessly locate objects or people inside a building. Generally the products offered under this term do not comply with the International standard ISO/IEC 24730 on real-time locating systems (RTLS). There is currently no de facto standard for an IPS systems design, so deployment has been slow. Nevertheless, there are several commercial systems on the market.
Instead of using satellites, an IPS relies on nearby anchors (nodes with a known position), which either actively locate tags or provide environmental context for devices to sense. The localized nature of an IPS has resulted in design fragmentation, with systems making use of various optical, radio, or even acoustic technologies.
Systems design shall take into account that an unambiguous locating service will require at least three independent measures per target. For smoothing to compensate for stochastic errors there must be a mathematical over-determination that allows for reducing the error budget. Otherwise the system must include information from other systems to cope for physical ambiguity and to enable error compensation.
- 1 Applicability and precision
- 2 Relation to GPS
- 3 Locating and positioning
- 4 Locating and tracking
- 5 Identification and segregation
- 6 Wireless technologies
- 7 Mathematics
- 8 Uses
- 9 See also
- 10 External links
- 11 References
Applicability and precision
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 radiofrequency 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.
With detailed reading in the marketing documents and even in the specifications served by many of the IPS vendors, the interested customer will look for details on precision, reproducibility and other terms for quality of function with little success. Many vendors do not even tangle with the term accuracy.
Relation to GPS
Global navigation satellite systems (GPS or 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 positioning signals ubiquitous (can be obtained everywhere), integration between GPS and indoor positioning can be made.
Currently, GNSS receivers are becoming more and more sensitive due to ceaseless progress in chip technology and 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. 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, proper coverage for the required four satellites to locate a receiver is not achieved with all current designs (2008–11) for indoor operations. Beyond, the average error budget for GNSS systems normally is much larger than the confinements, in which the locating shall be performed.
Locating and positioning
In contrast to the common title suggesting that a position may be affected by a system, this assumption is not correct: Despite naming, most of the IPS do not position an object, but just detect location of an object, not including the detection of the orientation or direction of that object. All known indoor positioning systems (IPS) neither affects nor detects a direction nor offers the option to change the position. Also other various systems titled as e.g. local positioning system and so on do not offer other but detecting an object in a certain known fixed location, report a measured location or just report the presence of the object in such location.
Locating and tracking
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 irritant sequence of jumps.
Identification and segregation
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, so many systems take advantage of existing infrastructure. Others provide increased accuracy at the expense of costly equipment and installations.
Choke point concepts
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) 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.
Long range sensor concepts
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 a bunch of sensors and overlapping reach.
Angle of arrival
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 to find the location relative to two anchor transmitters.
Time of arrival
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 to find a location. This is the technique used by GPS. 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. 
Received signal strength indication
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 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 by using statistics to filter out the inaccurate input data. Wi-Fi Positioning Systems are sometimes used outdoors as a supplement to GPS on mobile devices, where only few erratic reflections disturb the results.
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, sometimes referring to maps or other additional sensors to constrain the inherent sensor drift encountered with inertial navigation. Inertial measures generally cover the differentials of motion, hence the location gets determined with integrating and thus requires integration constants to provide results.
- Radio frequency identification (RFID): passive tags are very cost-effective, but do not support any metrics
- Ultrawide band (UWB): reduced interference with other devices
- Infrared (IR): previously included in most mobile devices
- Visible light communication (VLC): can use existing lighting systems
- Ultrasound: waves move very slowly, which results in much higher accuracy
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:
Advanced systems combine more accurate physical models with statistical procedures:
- Bayesian statistical analysis (probabilistic model)
- Kalman filtering (for estimating proper value streams under noise conditions).
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:
- Augmented reality
- School campus
- Guided tours of museums
- Shopping mall maps.
- Store navigation 
- Warehouse 
- Airport, bus, train and subway stations maps
- Car location in big or multi-storey indoor parkings.
- Targeted advertising 
- Social networking
- Hospital 
- Emergency response and plans 
- Other public building maps
- Automatic vehicle location
- Bluetooth SMART
- City furniture
- Fuzzy locating system
- GSM localization
- Indoor mapping
- Indoor parking
- MALT: Micromapping, Advertising, Location and ID, and Transactions
- Near field communication (NFC)
- Real-time locating system (RTLS)
- Robotic mapping
- QR code
- Sensor Fusion
- Wi-Fi positioning system
- Inside GPS
- Kevin Curran, Eoghan Furey, Tom Lunney, Jose Santos, Derek Woods and Aiden Mc Caughey (2011) An Evaluation of Indoor Location Determination Technologies. Journal of Location Based Services Vol. 5, No. 2, pp: 61-78, June 2011, ISSN: 1748-9725, DOI:10.1080/17489725.2011.562927, Taylor & Francis
- Eoghan Furey, Kevin Curran and Paul Mc Kevitt (2012) HABITS: A Bayesian Filter Approach to Indoor Tracking and Location. International Journal of Bio-Inspired Computation (IJBIC) Vol. 4, No. 2, pp: 79-88, ISSN: 1758-0366, DOI: 10.1504/IJBIC.2012.047178, InderScience
- 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.
- 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.
- Chiou Y, Wang C, Yeh S. 2010. An adaptive location estimator using tracking algorithms for indoor WLANs. Wireless Networks 16(7):1987-2012.
- Lim H, Kung L, Hou JC, Luo Haiyun. 2010. Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure. Wireless Networks 16(2):405-20.
- 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.
- 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
- Schweinzer H, Kaniak G. 2010. Ultrasonic device localization and its potential for wireless sensor network security. Control Engineering Practice 18(8):852-62.
- 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.See publication here
- Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad and Maimunah Sapri (2011). Ubiquitous Positioning: A Taxonomy for Location Determination on Mobile Navigation System. Signal & Image Processing: An International Journal.Vol 2: No.1,pp: 24-34. 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), Penang, MALAYSIA. See publication here, or click here if broken link
- Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Maimunah Sapri and Mohd Adly Rosly (2012). Investigation of Color Constancy for Ubiquitous Wireless LAN/Camera Positioning: An Initial Outcome. International Journal of Advancements in Computing Technology, Vol. 4, No. 7, pp. 269-280, See publication here
- Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad, Maimunah Sapri and Mohd Adly Rosly (2012). Performance Evaluation of Mobile U-Navigation based on GPS/WLAN Hybridization. Journal of Convergence Information Technology(JCIT), Vol. 7, No. 12, pp. 235-246, See publication here
- GNSS Indoors — Fighting The Fading
- Eoghan Furey, Kevin Curran and Paul Mc Kevitt (2012) Probabilistic Indoor Human Movement Modeling to Aid First Responders. Journal of Ambient Intelligence and Humanized Computing Vol. 3, No. 4, ISSN: 1868-5137, DOI: 10.1007/s12652-012-0112-4, Springer
- Pourhomayoun; Jin; Fowler (2012). "Spatial Sparsity Based Indoor Localization in Wireless Sensor Network for Assistive Healthcare Systems". EMBC2012.
- C.R. Comsa, et al.,“Source Localization Using Time Difference Of Arrival Within A Sparse Representation Framework”, ICASSP, , 2011.
- Sensor fusion and map aiding for indoor navigation
- Pedestrian localization for indoor environments
- Lee, 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 See publication here
- Indoor location is also useful in outer
- Indoor location and sports
- Using Indoor location and positioning systems to improve emergency plans