Others wireless technologies[edit]
- Radio frequency identification[16] (RFID): passive tags are very cost-effective, but do not support any metrics
- Ultra-wideband[17][53] (UWB): reduced interference with other devices
- Infrared (IR): previously included in most mobile devices
- Gen2IR (second generation infrared)
- Visible light communication[10][54] (VLC), as LiFi: can use existing lighting systems
- Ultrasound:[18] waves move very slowly, which results in much higher accuracy
- MALLY by Geniusmatcher is a hardware-free solution in any indoor location.
Other technologies[edit]
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.
Magnetic positioning[edit]
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.[55]
Inertial measurements[edit]
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,[56] 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.[57][58][59][60] However, in order to make it capable to build map itself, the SLAM algorithm framework [61] will be used.[62][63] [64]
Inertial measures generally cover the differentials of motion, hence the location gets determined with integrating and thus requires integration constants to provide results.[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.[67] Based on the motions and users' walking behaviors, IPS is able to estimate users' locations by machine learning algorithms.[68]
Positioning based on visual markers[edit]
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.[69][70]
Location based on known visual features[edit]
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.
Mathematics[edit]
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.
Empirical method[edit]
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).
Mathematical modeling[edit]
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) [71]
- Kalman filtering[14] (for estimating proper value streams under noise conditions).
- Sequential Monte Carlo method (for approximating the Bayesian statistical models).[72]
Uses[edit]
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.[73]
- Augmented reality[74]
- School campus
- Museum guided tours[75]
- 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[76]
- Tourism
- Amusement Parks
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