Indoor positioning using time of flight with respect to WiFi access points

The animation above shows floor plans of three levels of a building with six “responders” (WiFi access points advertizing IEEE 802.11mc FTM RTT) shown in green, and the estimates of positions computed by an Android smartphone shown in red, as it is carried upstairs and around floors. The position is recovered in 3-D and then quantized to floor level for plotting. Here the floor plan is only for visualization and not used in the computation of the position of the phone. As a result, the red dot may at times appear to pass through a wall or even appear outside the building.

The final position accuracy is about a meter or two (aside from the occasional outlier), while the raw distance measurements have a 10% to 90% CDF (cumulative distribution function) range of between say 0.2 and 0.8 meter, depending on circumstances (see Measurement Error). The “dilution of precision” (noise gain), depends on the positions of the responders, and varies throughout the volume. Optimal placement of responders is an open problem (see FTM RTT Placement), as is the best way to deal with the measurement errors.

Sample videos of FTM RTT indoor localization:

Walking around a classroom (Holmes Hall 389, University of Hawaii, Manoa) using 3 APs supporting IEEE 802.11mc (FTM RTT).
Relatively easy problem due to openness of the space, cooperating APs, high density of APs, and 2-D topology.

Wandering around one floor of a residential building with dividing walls (about 8 m x 10 m) [1].
A bit harder due to presence of some retarders (*) and attenuators.

Overlay of rate vector densities used in Bayesian update.
This gives a feel for the quality of measurement data and its change with time — when nothing is moving.

Wandering around a large house (about 12 m x 20 m) with 3 APs [2].
This example is more difficult because of the considerably lower density of APs,
as well as the distribution of retarding and attenuating materials (including some reinforced concrete).

Three-dimensional problem: wandering around a three-story residence with obstructions (walls, floors, furniture etc.) [1, 2].
First 3-D indoor localization based on FTM RTT observations.

Uncooperative APs in large box store (about 80 m x 120 m) [4].
First indoor localization using RTT w.r.t. APs that do not support IEEE 802.11mc.
More difficult because most measurements are not line of sight and are, as a result, dominated by refractive retardation (*)

(*) Refractive retardation: Construction materials, and merchandise on shelves, have large relative permittivity \(\varepsilon_{\mathrm {r} }\) — in the range 2–40 — (with relative permeability \(\mu_{\mathrm {r} }\) around 1). The equivalent refractive index \(n\) is proportional to the square root of the product of permittivity and permeability, i.e. \( n={\sqrt {\varepsilon _{\mathrm {r} }\mu _{\mathrm {r} }}} \). The speed of propagation of electromagnetic radiation is inversely proportional to the refractive index. As a result, FTM RTT “distances” are larger than the actual distances, often much larger.

Publications on indoor localization using FTM RTT:

Why not just use Signal Strength (RSSI)?

WiFi Fine Time Measurement (FTM) Round Trip Time (RTT)

Recovering position from distance measurements

Recovering position using Bayesian grid update

Where to place the responders?

Measurement Error

Peculiarities of FTM RTT — the “Position-Dependent Error”

Some Issues, Problems and Current Limitations

Which Wi-Fi access points support the FTM RTT protocol?

Some Related Material and Web Links

You can download the WifiRttScan App from Google (the app will only install on phones that support FTM RTT and run Android Pie or later). They say the following: “The WifiRttScan app is a research, demonstration, and testing tool for developers, vendors, universities, and more. With this app it is possible to obtain a 1-2 meter range accuracy to nearby WiFi RTT (IEEE 802.11mc) capable access points. This is especially useful indoors where GPS is not available. Developers, OEMs and researchers can use this tool to validate range measurements enabling the development of positioning, navigation and context-aware applications based on the WiFi-RTT API.”

See also WifiRttScanX an app based on the above but with more features and easier access to the log files.