When FTM Discovered MUSIC: Accurate
WiFi-based Ranging in the Presence of Multipath
Kevin Jiokeng
∗
, Gentian Jakllari
∗
, Alain Tchana
†
and Andr
´
e-Luc Beylot
∗
∗
IRIT-INPT/ENSEEIHT, University of Toulouse, France - Email: name.surname@enseeiht.fr
†
ENS Lyon, France - Email: name.surname@ens-lyon.fr
Abstract—The recent standardization by IEEE of Fine Timing
Measurement (FTM), a time-of-flight based approach for ranging
has the potential to be a turning point in bridging the gap between
the rich literature on indoor localization and the so-far tepid
market adoption. However, experiments with the first WiFi cards
supporting FTM show that while it offers meter-level ranging in
clear line-of-sight settings (LOS), its accuracy can collapse in
non-line-of-sight (NLOS) scenarios.
We present FUSIC, the first approach that extends FTM’s LOS
accuracy to NLOS settings, without requiring any changes to the
standard. To accomplish this, FUSIC leverages the results from
FTM and MUSIC – both erroneous in NLOS – into solving the
double challenge of 1) detecting when FTM returns an inaccurate
value and 2) correcting the errors as necessary. Experiments in 4
different physical locations reveal that a) FUSIC extends FTM’s
LOS ranging accuracy to NLOS settings – hence, achieving its
stated goal; b) it significantly improves FTM’s capability to offer
room-level indoor positioning.
Index Terms—FTM, NLOS, MUSIC, Indoor localization
I. INTRODUCTION
WiFi-based positioning traces it roots to the work on
RADAR [1] almost two decades ago. Its basic premise was to
leverage the ubiquitous WiFi infrastructure for delivering meter-
level localization indoors, where GPS usually is not accessible.
It proposes to localize mobile computing devices by estimating
the distances to WiFi access points whose locations are known.
In the years since, indoor localization has emerged as a major
scientific and technological challenge. Dozens of approaches
have been proposed representing a solution space that has
grown richer with time, to include a variety of underlying
technologies – UWB [2], sensors [3], acoustic anchors [4]
– as the mobile computing devices have evolved to include
smartphones, tablets, RFID tags, wearables and even micro-
implants [5]. Nevertheless, RADAR’s basic premise remains
true to this day: WiFi is ubiquitous, making it a prime platform
for indoor positioning. Recently, WiFi-based systems [6], [7]
have broken the meter-level barrier, promising an impressive
decimeter-level accuracy.
Unfortunately, it suffices for one to check their smartphone to
realize that market adoption, despite the involvement of industry
heavy-weights like Google and Microsoft [8], is lagging far
behind.
Against this backdrop, IEEE decided recently to put its
weight behind WiFi-based positioning. As part of the 802.11mc
amendment [9], it standardized FTM (Fine Timing Mea-
surement), a time-of-flight (ToF) based approach [10] for
computing the distance between a WiFi client and an access
Fig. 1: Distance estimation with FTM. Ground truth at 5 m.
point. It promises meter-level accuracy, inferior to some
recent works [7], but sufficient for many applications, such as
smart home occupancy [6] or shopping mall navigation. More
importantly, a standardized and native firmware implementation
using clocks with picosecond resolution, can make WiFi FTM
a major turning point in the indoor positioning becoming a
standard service on our mobile devices. While 802.11mc is
not supported by all WiFi devices currently deployed, it has
already gained the support of major WiFi manufacturers [11],
and it is adopted by the Android operating system [12]. The
recent Google Pixel 2 and 3 phones, for example, are 802.11mc-
compliant.
While in theory WiFi FTM is looking like a breakthrough
moment, the reality is more mixed. Consider the simple case
of a user standing
5 m
from an access point with FTM support
and collecting readings on a device with a WiFi FTM card.
Fig. 1 shows that, for the first
30 s
while the user is facing
the access point and there is a clear line of sight (LOS), FTM
estimates almost perfectly the distance between the user and
the access point. It suffices, however, for the user to turn 180
◦
,
thus obstructing the line of sight between the client and the
access point, for the FTM’s accuracy to collapse. This can be
explained by the presence of multipath indoors. Starting at time
30 s
, the signal following the line of sight is attenuated by the
presence of the human body, leading FTM to estimate distance
based on a (stronger) reflected signal. This weakness of WiFi
FTM in non-line-sight scenarios (NLOS) was recently showed
in [13], however, no solution was proposed. MUSIC (MUltiple
SIgnal Classification) [14], may seem like the natural approach
to resolving the multiple paths and compute the time-of-flight
of the direct path. Unfortunately, multiple studies [15], [16]