Indoor positioning system
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.
[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.
[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.
[20] There also exist technologies for detecting magnetometric information inside buildings or locations with steel structures or in iron ore mines.
Integration of data from various navigation systems with different physical principles can increase the accuracy and robustness of the overall solution.
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.
Statistical methods then serve for smoothing the locations determined in a track resembling the physical capabilities of the object to move.
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.
Simple concept of location indexing and presence reporting for tagged objects, uses known sensor identification only.
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.
AoA is usually determined by measuring the time difference of arrival (TDOA) between multiple antennas in a sensor array.
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.
[50] 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.
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[52]) 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.
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.
Unfortunately, Wi-Fi signal strength measurements are extremely noisy, so there is ongoing research focused on making more accurate systems Non-radio technologies can be used for positioning without using the existing wireless infrastructure.
Un-optimized compass chips inside smartphones can sense and record these magnetic variations to map indoor locations.
The MEMS inertial sensors suffer from internal noises which result in cubically growing position error with time.
[65][66] 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.
[69][70] 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.
