Sub-Nanosecond Time of Flight on Commercial Wi-Fi Cards

Abstract

Time-of-flight, i.e., the time incurred by a signal to travel from transmitter to receiver, is perhaps the most intuitive way to measure distances using wireless signals. It is used in major positioning systems such as GPS, RADAR, and SONAR. However, attempts at using time-of-flight for indoor localization have failed to deliver acceptable accuracy due to fundamental limitations in measuring time on Wi-Fi and other RF consumer technologies. While the research community has developed alternatives for RF-based indoor localization that do not require time-of-flight, those approaches have their own limitations that hamper their use in practice. In particular, many existing approaches need receivers with large antenna arrays while commercial Wi-Fi nodes have two or three antennas. Other systems require fingerprinting the environment to create signal maps. More fundamentally, none of these methods support indoor positioning between a pair of Wi-Fi devices without third party support.In this paper, we present a set of algorithms that measure the time-of-flight to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these algorithms and demonstrate a system that achieves accurate device-todevice localization, i.e. enables a pair of Wi-Fi devices to locate each other without any support from the infrastructure, not even the location of the access points.

Toward Fine-Grained Indoor Localization Based on Massive MIMO-OFDM System: Experiment and Analysis

“…The intuitive solution is to have more bandwidth available, which is difficult (except for the milliwave band) due to the spectrum scarcity in the sub-6 GHz band. In [34], a prototype Chronos was proposed to merge the measurement from several separated bands and stitch them together to give the illusion of a wideband radio. In this way, the authors achieved sub-nanosecond ToF accuracy using WiFi cards.…”

CRLB-based Positioning Performance of Indoor Hybrid AoA/RSS/ToF Localization

“…The observation standard deviation of AoA is related with specific indoor environment [5], [17]. In the simulations, σ αi RSS, the standard deviation can be set to σ P j = 3 dB [18], [19], ∀j ∈ 1, 2, • • • , n. According to [5], [7], the standard deviation of ToF is correlated with the distance between AP and mobile node, as well as the propagation in the line-of-sight (LoS) or non-line-of-sight (NLoS) situation. An empirical model of σ τ k (k ∈ 1, 2, • • • , l) in nanosecond, is recommended by [5] as follows:…”

“…In [6], based on Decawave's DW1000 impulse radio UWB IC, the Decawave's AoA estimation demo was described. Together with the ToF estimation scheme in [7], such kind of hardware would pave the way for hybrid time-based and AoA-based indoor localization with the aim of a finer-grain positioning performance.…”

A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization

“…Based on CSI, some research works can exploit ToA to locate the user's position by trilateration method [14], [15]. In [14], a set of algorithms named Chronos is proposed to estimate the ToA relying on the phase values after inverse fast Fourier transform (IFFT) of the original CSI. A hopping method between multiple frequency bands is utilized to increase the accuracy of estimated ToA.…”