Wi-Fi positioning system
[3] Wi-Fi positioning takes advantage of the rapid growth in the early 21st century of wireless access points in urban areas.
[4] The most common technique for positioning using wireless access points is based on a rough proxy for the strength of the received signal (received signal strength indicator, or RSSI) and the method of "fingerprinting".
[8] The possible signal fluctuations that may occur can increase errors and inaccuracies in the path of the user.
To minimize fluctuations in the received signal, there are certain techniques that can be applied to filter the noise.
[9] Accurate indoor localization is becoming more important for Wi‑Fi–based devices due to the increased use of augmented reality, social networking, health care monitoring, personal tracking, inventory control and other indoor location-aware applications.
[10][11] In wireless security, it is an important method used to locate and map rogue access points.
Many techniques exist to accomplish this, and these may be classified based on the four different criteria they use: received signal strength indication (RSSI), fingerprinting, angle of arrival (AoA) and time of flight (ToF).
[14] RSSI localization techniques are based on measuring rough relative signal strength at a client device from several different access points, and then combining this information with a propagation model to determine the distance between the client device and the access points.
[11][14] Though one of the cheapest and easiest methods to implement, its disadvantage is that it does not provide very good precision (median of 2–4m), because the RSSI measurements tend to fluctuate according to changes in the environment or multipath fading.
[16] Monte Carlo sampling is a statistical technique used in indoor Wi-Fi mapping to estimate the location of wireless nodes.
The process involves creating wireless signal strength maps using a two-step parametric and measurement-driven ray-tracing approach.
This method has been found to provide good location estimates of users with sub-room precision using received signal strength indication (RSSI) readings from a single access point.
[18] Traditional fingerprinting is also RSSI-based, but it simply relies on the recording of the signal strength from several access points in range and storing this information in a database along with the known coordinates of the client device in an offline phase.
[20] With the advent of MIMO Wi-Fi interfaces, which use multiple antennas, it is possible to estimate the AoA of the multipath signals received at the antenna arrays in the access points, and apply triangulation to calculate the location of client devices.
SpotFi,[14] ArrayTrack[10] and LTEye[21] are proposed solutions which employ this kind of technique.
[14] Therefore, the following complex exponential can be used as a simplified representation of the phase shifts experienced by each antenna as a function of the AoA of the propagation path:[14]
[14] OFDM transmits data over multiple different sub carriers, so the measured received signals
[14] The AoAs can then be deduced from this matrix and used to estimate the position of the client device through triangulation.
Time of flight (ToF) localization approach takes timestamps provided by the wireless interfaces to calculate the ToF of signals and then use this information to estimate the distance and relative position of one client device with respect to access points.
[14] Typical applications for this technology are tagging and locating assets in buildings, for which room-level accuracy (~3m) is usually enough.
[24] The time measurements taken at the wireless interfaces are based on the fact that RF waves travel close to the speed of light, which remains nearly constant in most propagation media in indoor environments.
Therefore, the signal propagation speed (and consequently the ToF) is not affected so much by the environment as the RSSI measurements are.
[23] As in the RSSI approach, the ToF is used only to estimate the distance between the client device and access points.
Then a trilateration technique can be used to calculate the estimated position of the device relative to the access points.
[24] The greatest challenges in the ToF approach consist in dealing with clock synchronization issues, noise, sampling artifacts and multipath channel effects.
[15] More recently, the Wi-Fi Round Trip Time standard has provided fine ToF ranging capabilities to Wi‑Fi.
As of 2019[update], French law requires drones weighing more than 800 grams to broadcast their GPS coordinates, speed, heading, aircraft type, and serial numbers via Wi-Fi.
[25] The data is encoded in Wi-Fi beacon frames via an 802.11 vendor element (with the SGSDN's OUI of 6A:5C:35[26]) containing TLV-encoded subfields specified in the legislation.
[27] Although the data format is intended for use in moving aerial vehicles, it can easily be adapted to static Wi-Fi access points (i.e. by setting the horizontal speed field to zero).
[28] Appending "_nomap" to a wireless access point's SSID excludes it from Google's WPS database.
