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High-accuracy Positioning for Indoor Applications: RFID, UWB, 5G, and beyond (Invited Paper)

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Highly accurate and reliable indoor positioning— at accuracy levels in the 10 cm range—will enable a large a number of innovative location-based applications because such accuracy levels essentially allow for a useful real-time interaction of humans and cyber-physical systems. Activity recognition, navigation at " shelf " level, geofencing, process monitoring and process control are among the envisioned services that will yield numerous applications in various domains. This paper reviews the difficulties faced by indoor positioning systems, motivating the requirement for a large signal bandwidth and how a lack of bandwidth can be compensated by multi-antenna systems. The potential capabilities of upcoming generations of wireless systems will increasingly make high-accuracy positioning available in near future.
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High-accuracy Positioning for Indoor Applications:
RFID, UWB, 5G, and beyond
(Invited Paper)
Klaus Witrisal, Stefan Hinteregger, Josef Kulmer, Erik Leitinger, and Paul Meissner
Graz University of Technology, Austria
email: {witrisal, stefan.hinteregger, kulmer, erik.leitinger, paul.meissner}@tugraz.at
Abstract—Highly accurate and reliable indoor positioning—
at accuracy levels in the 10 cm range—will enable a large a
number of innovative location-based applications because such
accuracy levels essentially allow for a useful real-time interaction
of humans and cyber-physical systems. Activity recognition,
navigation at “shelf” level, geofencing, process monitoring and
process control are among the envisioned services that will yield
numerous applications in various domains. This paper reviews
the difficulties faced by indoor positioning systems, motivating
the requirement for a large signal bandwidth and how a lack of
bandwidth can be compensated by multi-antenna systems. The
potential capabilities of upcoming generations of wireless systems
will increasingly make high-accuracy positioning available in near
future.
I. INTRO DUC TIO N
Robust and accurate indoor positioning is a key enabler
for a wealth of future location-based services, ranging from
supply-chain management and manufacturing to health-care
and entertainment.
For example in healthcare, application examples include
behavioral monitoring to assess the physical and mental health
of individuals, emergency (fall) detection to alert caretakers
or emergency services, real-time assistance to provide context
awareness to medication management systems (to remind—
for instance—to take medications before/during/after meals) or
as an orthotic and rehabilitation tool for individuals suffering
from cognitive decline, geofencing for people with dementia,
and even as a navigation aid for visually impaired (see [1],
[2] and the references therein).
In manufacturing and logistics, real-time positioning can be
used to monitor the flow of items and hence the progress of
processes. It can also be used to control processes in real time,
for example the parametrization of tools has been envisioned,
and hence to improve the efficiency and detect anomalies.
Therefore, position information is a vital component of so-
called smart factories [3]–[5]. However, sufficient reliability
of the positioning service is required for that purpose.
In a smart sales-floor, products and user devices can be
localized to realize a recommender systems similar as in an
internet store [6], [7]. In this scenario, the customer may
be navigated to the desired items, matching accessories or
alternative choices can be recommended, a real-time inventory
function can be realized that ensures the shop owner always
This work was supported by the Austrian Research Promotion Agency
(FFG) within the project REFlex (project number: 845630).
knows what items are still on stock, and last-but-not-least, theft
control can be realized. To implement these functions, it will
be of key importance to recognize interactions between cus-
tomers and (identified) tracked objects, which is the challenge
to be addressed in this scenario.
These are a few specific examples that require cm or dm-
level accuracies to yield robust activity recognition. Such
a performance level cannot be achieved with current mass-
market technologies. E.g. current RFID systems are already
used for some of these applications, however with shortcom-
ings concerning read range, false detections/missed detection
due to multipath, and only imprecise positioning. Another
difficulty is the heterogeneity of the scenarios and application
environments. Therefore, as of today, the technologies for
indoor localization have not converged towards a unique
winning approach.
Radiopositioning is—in principle—a very promising sens-
ing method, because radio transceivers can be integrated in
existing devices like smartphones and built at small form
factors with low power consumption. Among the competing
modalities [8]–[14], video cameras and microphones [15]–
[17], for example, suffer from occlusions and a lack of
acceptance because of privacy concerns. For radio systems,
on the other hand, the influence of the dense multipath radio
channel in indoor environments still makes accurate and robust
positioning a challenging task.
This paper first reviews the importance of a large signal
bandwidth for accurate indoor positioning. We discuss the
role of multi-antenna system configurations which can partly
compensate for a lack of bandwidth. The diversity gains
leveraged in MIMO (multiple-input multiple-output) radars
[18], [19] can also be exploited in dense multipath channels,
as faced in indoor environments, in order to obtain accurate
positioning of RFID transponders [20].
Ultra-wideband (UWB) signals yield excellent accuracy,
since they allow for a separation of the multipath components
(MPCs) [21]–[24]. Hence, on the one hand, the direct signal
path can be isolated from interfering MPCs; on the other hand,
position-related information becomes accessible as well in
later-arriving MPCs and turned into an advantage, as discussed
in the second part of this paper. We will argue that with the
advent of mm-wave communications in the 60 GHz band [25]–
[27], a UWB localization system could operate synergetically
with an existing communication system, e.g. using the IEEE
−600 0 600
0
0.02
0.04
time/distance [m]
sample functions
BW = 1 MHz: flat fading
−60 0 60
0
0.05
0.1
0.15
time/distance [m]
BW = 10 MHz
−6 0 6
0
0.2
0.4
time/distance [m]
BW = 100 MHz
−0.6 0 0.6
0
0.5
1
time/distance [m]
BW = 1 GHz: UWB
with multipath
sample 2
sample 3
sample 4
sample 5
line−of−sight
Fig. 1. Sample functions illustrating the ranging problem under DM over a wide range of BWs (neglecting AWGN).
802.11ad standard [28]. Beamforming technologies proposed
for these systems [25] perfectly complement the needs of
the localization system and vice versa: also the beamforming
algorithms will benefit from the location information and from
environmental radio maps, i.e. spatial characterizations of the
propagation channel that can be estimated and tracked in
realtime. Location awareness is created, which is expected to
play an important role in future communication systems [29].
We finally speculate about the application of a UWB cog-
nitive radar for the accurate, robust, and efficient positioning
of RFID tags in challenging indoor environments.
II. RANGING AND PO SIT ION ING I N DENS E MULTIPATH
This section illustrates the influence of the signal bandwidth
on the potential ranging accuracy and shows that multi-antenna
configurations can compensate for a lack of bandwidth.
Fig. 1 illustrates the effects of dense multipath [30]. The
channel is modeled as a hybrid deterministic-stochastic chan-
nel model (GSCM) with the line of sight (LOS) component
as deterministic component and all other components as dense
or diffuse multipath (DM). This DM interferes with the LOS
component and leads to multipath effects such as amplitude
fading and pulse distortion. For the UWB case, the DM is
well-separated from the LOS thus neither amplitude fading
nor pulse distortion impair the received signal. Here, very
accurate ranging can easily be achieved, also due to the short
temporal extent of the UWB pulse. In the narrowband case
the complete DM interferes with the LOS component and only
amplitude fading occurs. The lack of distortion is beneficial but
the “length” of the pulse of >100 m nevertheless makes this
configuration useless for indoor positioning. In-between these
cases both amplitude fading and pulse distortion deteriorate
the received signal.
A. Ranging Error Analysis and Diversity Gain
In [31] we developed the Cr´amer Rao lower bound (CRLB)
for the ranging and positioning problem for a channel with a
LOS component impaired by DM. The ranging error bound
(REB, CRLB for the ranging problem) is depicted in Fig. 2 and
compared to the standard deviation (STDV) of the estimation
error for two estimators.
A na¨ıve matched filter (MF, marked by circles) estimator,
convolving the received signal with the transmitted pulse and
searching for its maximum, deviates from the REB at about
100101102103
10−3
10−2
10−1
100
101
102
bandwidth [MHz]
estimation error [m]
Channel: KLOS = 1; γrise = 5 ns; γdec = 20 ns; ELOS/N0 = 30 dB
simul. SISO max. likelihood
simul. SISO matched filter
simul. 4x4; max. likelihood
simul. 4x4; matched filter
REB SISO
REB 4x4
Fig. 2. Ranging Error Bound and range estimation standard deviations of
different estimators. Channel Parameters: KLOS = 0 dB, τrms = 17.5ns
500 MHz due to a positive bias and estimation outliers induced
by the DM. Proper consideration of the DM by whitening the
received signal prior to the estimation of the ToA is highly
beneficial. This maximum likelihood (ML, marked by ‘+’)
estimator deviates at about 100 MHz from the REB.
At even lower bandwidth, the ML estimator fails and the
MF estimator becomes more robust. This can be explained
by the impact of the DM on the ranging solution: If the
so-called signal to interference plus noise ratio (SINR) [31]
drops below a certain threshold, the ML estimator cannot
detect the LOS component in the resulting noise floor after
whitening. (The SINR denotes the ratio of the useful LOS
signal to the DM interfering with it.) For narrowband signals,
the complete DM process interferes with the LOS pulse and
the MF estimator can make use of the DM in the sense that it
exploits the power in the DM process in addition to the power
in the LOS component. Thus only fading and AWGN impair
the ranging accuracy as long as the instantaneous signal-to-
noise-ratio (SNR) is high enough. However, as can be seen in
Fig. 2, the accuracy for small bandwidth is at a poor level.
The results suggest a potential ranging STDV in the order
of 15 cm at 100 MHz, in a single- input single-output (SISO)
configuration.
By introducing multiple antennas at the transmitter and re-
ceiver sides, a multiple-input multiple-output (MIMO) system
can be realized, which exploits diversity in a non-coherent
−1 0 1 2 3 4 5 6
−1
0
1
2
3
4
5
6
x
y
2x monostatic
4x bistatic
16x bistatic
Fig. 3. Position error bound (2-fold STDV ellipses) and MIMO gain on
a backscatter channel with a bandwidth of 50 MHz. The different antenna
configurations are discussed in the text. Channel Parameters: KLOS = 0 dB,
τrms = 17.5ns
fashion [31]. Such a MIMO system shows two gains depicted
in Fig. 2. The first gain is an accuracy improvement seen by
comparing the REB for the SISO and the 4x4-MIMO system.
This improvement is inversely proportional to the square root
of the number of measurements, leading to a factor of 1
4
for the 4x4-MIMO system. The second gain is a detection
improvement of the LOS component which is coupled with the
SINR. This gain makes the estimation more robust to outliers
and thus the REB is achieved at lower bandwidths (for a more
detailed discussion and a comparison with measured data see
[32]). With the 4x4-MIMO system and the ML estimator, a
potential ranging STDV in the order of 25 cm can be achieved
at 20 MHz in comparison to an STDV of 4 m in case of the
SISO configuration (MF) (see the green arrow).
B. MIMO Gain for RFID Positioning in Dense Multipath
In an RFID system, accurate range measurements can
be conducted by superimposing wideband or ultra-wideband
signals with the regular (e.g. UHF) interrogation signals. In
[33] it was suggested to use a direct-sequence spread spectrum
signal for this purpose with a bandwidth of approximately
50 MHz, while [34], [35] employ UWB signals. The probing
signal undergoes a backscatter radio channel, which—for the
analysis of the positioning performance—can be modeled as
the convolution of uplink and downlink channels [20], [36].
Two cases have to be distinguished: If a single antenna is used
for the TX and RX, the up and downlink channels are fully
correlated, while a configuration with separated TX and RX
antennas may see uncorrelated channels. In the latter case,
the diffuse multipath has only half the power density from
0 2 4 6
0
1
2
3
4
5
6
7
8
9
x[m]
y[m]
window
windows
windows
blackboard
wood-covered wall
metal door
concrete
pillar
concrete
walls
Anchor virtual
anchor
Fig. 4. Illustration of the MINT concept using a single anchor. Position-
related information provided by the LOS component (bold line) as well as by
numerous MPCs that can be associated to the geometry.
the former [36] with a beneficial effect to the ranging [20].
Introducing multiple TX and RX antennas at the readers,
MIMO gains can be exploited in a similar fashion as in a
MIMO radar [18], [19].
Fig. 3 illustrates the potential performance gain for a time-
of-flight-based positioning system employing a bandwidth of
50 MHz with channel parameters as in Fig. 2. The performance
is indicated in terms of the position error bound, which is
derived from the ranging error bound of Fig. 2 [20]. The
three ellipses—from largest to smallest—correspond to an-
tenna configurations with a single TX/RX-antenna per reader
(acquiring two “monostatic” range measurements over “fully
correlated” up/downlink channels), a pair of separated TX and
RX antennas per reader (acquiring four bistatic measurements
over uncorrelated channels), and two pairs of separated TX
and RX antennas per reader (yielding 16 bistatic measurements
over uncorrelated channels). The performance improvement is
by a factor of approx. 8in STDV, reaching a level of 15 cm
in the best case.
estimator
III. MULTIPATH -AS SIS TED INDOOR NAVI GATIO N AND
TRAC KI N G (M INT )
At ultra-wide signal bandwidths, it becomes possible to
resolve individual multipath components (MPCs), including
specular reflections that can be modeled deterministically.
These MPCs provide additional position-related information,
96
94
92
90
88
80
60
40
20
×10 -3
0
1
0.8
0.6
0.4
0.2
0
Anchor/LOS: 27.3 dB
right window: 6.1 dB
blackboard: 9.5 dB
left window: 17.2 dB
left-right win.: 8.8 dB
right-left win.: 9.4 dB
right-left-right win.: 6.5 dB
left-right-left win.: 9.4 dB
|r(τ)|
τ[ns]
Fig. 5. Impulse response analysis with estimated SINRs of the respective
MPCs, BW 2GHz, fc= 7 GHz.
i.e. they can be exploited to enhance the performance of
a positioning system. This is viable for indoor localization
systems, for which a reduction of the required infrastructure
is of key importance while keeping the required level of
accuracy and robustness. With properly designed algorithms,
even single-anchor configurations (within each room) are fea-
sible. To realize this multipath-assisted indoor navigation and
tracking (MINT) system, algorithms are needed which actively
take environmental propagation information into account. This
section discusses such algorithms and their performance.
A. Single-Antenna Terminals
Using an a-priori known floor plan, the arrival times of
specular deterministic MPCs can be modeled by virtual an-
chors (VAs), which are mirror images of the positions of the
physical anchors at known positions [37]–[41] (Fig. 4). DM
comprises all other—not geometrically modeled—propagation
effects included in the signals.
Fig. 4 illustrates the concept of MINT in a representative
environment. The estimated MPC delays are associated to
the expected delays according to the VA model (blue rays in
Fig. 4) such that they can be used for positioning. To properly
weigh the position-related information of each range measure-
ment [38], their SINR values are determined, which now define
the power ratios between the useful deterministic MPCs and
the DM interference plus noise at the corresponding delays.
The SINRs are related to the range uncertainties of the MPCs
that are associated with VAs [42], [43]. An online estimation of
these range uncertainties also allows for an efficient selection
of the VAs that provide reliable position-related information.
This is shown in [40], in which a simultaneous localization and
mapping (SLAM) approach is presented for MINT that omits
the requirement of an a-priori known floor plan and infers the
range uncertainties during the tracking.
−1 0 1 2 3 4 5 6
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x [m]
y [m]
−2
−1.8
−1.6
−1.4
−1.2
−1
CRLB EKF−DA Estimation points
Position error bound [log(m)]
Anchor
Fig. 6. Position error bound (PEB) and tracking results for bandwidth of
2GHz, and a single fixed anchor. The PEB has been computed from estimated
SINRs; gray crosses are 60 positions used for this SINR estimation [38]. Solid
and dashed ellipses denote the standard deviation ellipses corresponding to the
CRLB and to the error covariance matrices of an extended Kalman tracking
filter, respectively, at several points along two trajectories. These ellipses are
enlarged by a factor of 20 for better visibility. (c.f. [2])
Fig. 5 shows UWB signals (with 2GHz bandwidth) that
were measured in the scenario illustrated in Fig. 4. The delay
and amplitude tracks for several associated MPCs are shown.
From these, the corresponding SINRs have been estimated and
indicated in the legend. At such a large bandwidth, the LOS
component is almost entirely separated from later-arriving
MPCs and DM, which results in a high SINR value and hence
in a large amount of position-related information. Some later-
arriving MPCs also have high SINR values and are thus useful
for positioning.
An example how the SINR values (respectively the corre-
sponding range standard deviations) can be translated via the
geometry to a lower bound on the position estimation error is
illustrated in Fig. 6 [2], [43]. It can be seen that an error below
10 cm is achievable over almost the whole scenario with only
a single anchor. Also, the influence of the expected visibility
regions of important reflectors is observable. The performance
of an implementation of the MINT concept using an EKF with
data association (DA) is depicted by showing the estimated
position with uncertainty ellipses (gray) and the corresponding
CRLB ellipses (black). Both agree well, indicating the correct
weighting of the MPCs and confirming the usefulness of the
environmental model given by the SINRs.
−6 −5 −4 −3 −2 −1 0
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1
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4
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8
x[m]
y[m]
Agent 1
Agent 2
Agent 3
windows
right
wall
(concrete)
left wall
(plasterboard)
lower wall
(plasterboard) whiteboard
Fig. 7. Cooperative setup of three agents. The agents perform non-cooperative
(grey) and cooperative (blue) measurements and utilize the multipath propa-
gation for localization without the need of an anchor node.
B. Cooperative MINT
The previous section presented indoor navigation and track-
ing with reduced infrastructure. The agents localize themselves
utilizing the multipath propagation between an agent and
anchors. A promising way to further decrease the dependence
on infrastructure is cooperation of the agents [44], [45].
The agents share the belief about their position with their
neighbors and also perform cooperative measurements. This
scenario is shown in Figure 7. The mobiles use monostatic
measurements (gray) for non-cooperative self-localization by
emitting a pulse and receiving the corresponding reflections.
The bistatic (cooperative) measurements (blue) are performed
between neighboring mobiles. The agents use these measure-
ments for accurate and robust tracking without the need for
anchor nodes.
C. 5G Systems – mm-Waves and Beamforming
Fig. 8 shows the likelihood function for the multipath-
assisted positioning problem as a function of position [39],
evaluated over a floor plan [2]. In this scenario mm-wave
signals are used at a carrier frequency of 60 GHz, again with a
bandwidth of 2GHz, reflecting the proposed frequency range
for 5G systems [28]. It compares (a) LOS and (b) obstructed
LOS (OLOS) conditions with (c) OLOS with the use of
beamforming. The bold black lines indicate the directions to
the anchor, thin black lines the directions to first-order VAs,
and black dashed lines the directions to second-order VAs.
The black diamonds mark the estimated positions of the agent.
Using a maximum likelihood positioning algorithm as in [39],
an error in the centimeter level is achieved (2cm for the LOS
and 3cm for the OLOS situations).
The potential use of beamforming shows a different great
advantage: the multimodality of the likelihood function is
reduced, which reduces the risk of converging to a wrong
local maximum. Large modes at locations farther away from
the true agent position are suppressed due to the angular
resolution of the array antenna. Note, however, that MPC
delays are still responsible for providing a high accuracy in a
direction orthogonal to the LOS path. Without the processing
of multipath, we would see a smooth maximum (along the
circle) instead of a sharp peak. The likelihood function in Fig.
8c has been computed by using a phased-array beamformer for
each exploited MPC. This is achieved by coherently adding the
signals at the agent-side array positions, taking into account
the relative phase shifts that correspond to the known arrival
angles of the MPCs. The figure exemplary shows that such
a processing, envisioned for 5G mm-wave communication
systems, can greatly improve the robustness of the localization,
since many local maxima can be ruled out.
IV. COG NIT IVE PO SI T IO NIN G O F RFID TR ANS PO N DE RS
Given these multiple benefits, it is fair to anticipate that
multipath can also be useful for the accurate positioning of
RFID transponders. The use of UWB signals is a prerequisite
for this, which was proposed e.g. in [34], [35].
An intriguing approach towards this goal is the application
of a cognitive radar [46]–[48]. The RFID tags namely provide
a spatial sampling of the radio channel properties (at the tag
positions) which yields a map of the radio environment that
can be utilized for robust positioning and efficient resource
allocation—key properties of a cognitive radar. Visualize an
application that aims at locating the merchandise in a fashion
shop: Thousands of spatial samples may be collected through-
out the shop floor, yielding a detailed picture of the prop-
agation conditions. In [48] we propose to use time-reversal
processing based on a known model of the deterministic MPCs
to focus the transmitted UWB signals at the position of the
RFID transponders. This strategy has been shown to improve
the robustness in case of obstructed LOS paths. A cognitive
positioning system was recently described in [49].
V. CON CLU SIO NS AN D OUT L OO K
This paper envisions accurate and robust indoor localization
as a key enabler of future location-based services. Real-time
interaction between users, objects, and cyber-physical systems,
including the need for accurate recognition of activities and
identification of objects, is the key feature such applications
demand for.
The paper reviews the need for a large signal bandwidth to
achieve cm-level positioning in presence of dense multipath
as faced in indoor environments. Multi-antenna configurations
can significantly relax the need for high bandwidth since
0 1 2 3 4 5 6 7
0
1
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5
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7
x[m]
y[m]
right wall
cupboard
panel
Anchor
(a) log(p(r|ψ)) LOS
0 1 2 3 4 5 6 7
0
1
2
3
4
5
6
7
x[m]
y[m]
right wall
cupboard
panel
Anchor
(b) log(p(r|ψ)) OLOS
0 1 2 3 4 5 6 7
0
1
2
3
4
5
6
7
x[m]
y[m]
right wall
cupboard
panel
Anchor
(c) log(p(r|ψ)) OLOS, beamforming
Fig. 8. Likelihood function over the floor plan for (a) LOS, (b) OLOS situation, and (c) OLOS situation with phased-array beamforming. The position error
of the MLE is 2cm and 3cm for LOS and OLOS situations, respectively. Bold black lines show the directions to the anchors, thin black line the directions
to first-order VAs, and black dashed lines the directions to second-order VAs. The black diamonds mark the estimated positions of the agent. (c.f. [2])
diversity gain can be exploited. Furthermore at ultra-wide
bandwidth, individual multipath components can be resolved
and used as an additional source of position information.
The resulting “multipath-assisted” positioning system yields
higher robustness and higher accuracy at a relaxed need for
infrastructure (in form of anchor nodes).
Future 5G mm-wave communication systems could be an
ideal platform for achieving high-accuracy indoor localization
with this concept. In addition to a large signal bandwidth,
beamforming capabilities are expected, which make the lo-
calization and tracking more robust and efficient. It becomes
feasible to obtain accurate and robust indoor localization with
only a single anchor node in a room.
The positioning of RFID tags in dense multipath channels
could become the (missing) “killer” application for a cognitive
radar. A large number of RFID tags potentially yields an
accurate radio map of an environment that can be levaraged
by the cognitive radar for higher robustness and efficiency.
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