=Paper=
{{Paper
|id=Vol-3248/paper15
|storemode=property
|title=Peer-To-Peer UWB Ranges as a Source of Training Data for Estimating BLE RSSI Path-Loss Exponents
|pdfUrl=https://ceur-ws.org/Vol-3248/paper15.pdf
|volume=Vol-3248
|authors=Satinath Debnath,Kyle O'Keefe
|dblpUrl=https://dblp.org/rec/conf/ipin/DebnathO22
}}
==Peer-To-Peer UWB Ranges as a Source of Training Data for Estimating BLE RSSI Path-Loss Exponents==
https://ceur-ws.org/Vol-3248/paper15.pdf
Peer-To-Peer UWB Ranges as a Source of Training Data for
Estimating BLE RSSI Path-Loss Exponents
Satinath Debnath 1, Kyle O’Keefe2
1
University of Calgary, Department of Geomatics Engineering, 2500 University Dr. NW, Calgary, Alberta,
T2N1N4, Canada
2
University of Calgary, Department of Geomatics Engineering, 2500 University Dr. NW, Calgary, Alberta,
T2N1N4, Canada
Abstract
This paper presents a practical indoor positioning approach to improve short-range distance
estimation using Bluetooth received signal strength (RSSI) and precise ultra-wideband (UWB)
range measurement. The conventional distance estimation technique from Bluetooth RSSI uses
a classical path loss model that is environment specific. The model faces challenges in
determining suitable path loss factors to estimate distance accurately. A curve-fitting function
on BLE RSSI values with actual distance shows that the distance estimation error increases
with increasing distance. UWB provides a precise range measurement in line of sight (LOS)
condition indoors. Therefore, this paper investigates the feasibility of using UWB range as a
source of training data for BLE RSSI range estimation. The experimental results show that a
line-fitting model of RSSI values using a UWB range gives similar performance compared to
the actual distance for short ranges in a complex indoor environment.
Keywords
BLE-RSSI, Ultra-wideband, Indoor Positioning
1. Introduction
Bluetooth low energy (BLE) has recently emerged as a low power, low cost, low complexity
solution for determining the distance between two BLE equipped devices and has been identified as a
favored method for electronic contact tracing in the context of the ongoing Covid-19 pandemic [1].
BLE can indeed determine if two devices have been in proximity, the ability to estimate the actual range
between the devices is limited by the difficulty in converting an observed signal power observation into
a distance measurement. This conversion depends both on estimating an appropriate path loss exponent
for the environment and identifying obstructions and obstacles in the line-of-sight [2]. Most mobile
phones in the market today are equipped with Bluetooth radios and can measure Received Signal
Strength Indicator (RSSI). However, the available BLE-based contact tracing apps on them do not give
an accurate and reliable measured distance from BLE RSSI, as apps do not consider hardware and
environmental factors [3].While it is most often used for nearest-beacon proximity and fingerprinting
methods [4]–[8]. RSSI can be used to directly estimate range through estimation of a path-loss exponent
or application of other path-loss models, however, this suffers from drawbacks due to varying devices
and propagation environments [9], [10] . Consequently, Bluetooth localization using RSSI-based
ranges is not commonly used as it requires a large amount of training data in order to be useful [11].
The latest Apple’s iPhone series supports an ultra-wideband (UWB) ranging radio chip (U1), which
is 802.15.4z compliant, computes distances based on asymmetric double sided flight time between two
UWB enabled devices. UWB ranges have up to centimetre-level precision in line-of-sight (LOS)
conditions [12], [13] and generally allow for decimetre level accuracies.
IPIN 2022 WiP Proceedings, September 5-7, 2022, Beijing, China
EMAIL: satinath.debnath@ucalgary.ca (S. Debnath); kyle.okeefe@ucalgary.ca (K. O’Keefe)
ORCID: 0000-0003-2715-2488 (S. Debnath); 0000-0003-2123-2372 (K. O’Keefe)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
Our long-term objective is to develop a concept where a small group of devices or users equipped
with UWB ranging systems can be used to train low-cost systems that rely on BLE alone including
relative pedestrian navigation and smartphone-based contact tracing applications.
This paper proposes and presents preliminary results of using peer-to-peer UWB ranges as a source
of training data to fit path-loss models to Bluetooth RSSI measurements and assesses the accuracy of
UWB-trained path-loss to that obtained using a reference or truth range obtained through laser range
finding.
The rest of the paper is organized as follows. Section 2 describes the fundamentals of system:
Bluetooth and UWB technology in brief. Section 3 presents the proposed system. Section 4 discusses
preliminary experimental results. Finally, Section 5 summarizes current work and describes the scope
of future work.
2. Background
We propose to use Bluetooth Low Energy and a low-cost solution as it is already widely deployed.
Ultrawideband will serve as a source of more accurate training data.
Bluetooth RSSI-based indoor localization is the most attractive and widely used technology due to
low cost, low power consumption, small size, and ease of deployment [14]–[16]. Many manufacturers
have introduced BLE-based indoor positioning solutions [17]. The technology uses three advertising
channels primarily for discovery purposes: channel 37 (2402 MHz), channel 38 (2426 MHz), and
channel 39 (2480 MHz). When one BLE device measures RSSI values of another, these will differ on
each channel due to different channel gain and multipath fading. Some researchers and systems have
simply used all available RSSI values while others have noted that there is less variably when BLE
channels are considered separately instead of considering the aggregated channel’s RSSI [18], [19].
The signal strength of the transmitted radio wave attenuates as it travels away from the transmitter.
In three-dimensional free space that can be modelled as one over range squared [20]. However, in real
environments the loss may be more severe, due to attenuation, or less due to constructive interference
or wave-guide effects. If the path loss can be modelled as a function of distance, then the distance
between the transmitter and the receiver can be estimated from a received signal strength measurement.
The Free Space Friis Model is used as a basis to form a simplified standard log-distance path loss model
in an indoor environment.
𝒅𝒅
RSSI = RSSI (𝒅𝒅𝟎𝟎 ) - 10nlog + 𝑿𝑿𝝈𝝈 (1)
𝒅𝒅𝟎𝟎
where RSSI(d0) represents a reference RSSI value at the reference distance d0, typically 1 m, Xσ and n
represent the observation error and path loss exponent value respectively, and d is the distance between
transmitter and receiver. The path loss exponent is environment specific and usually determined by
either choosing a standard value or by fitting a line to training data [21], [22].
Ultra-wideband (UWB) is an emerging precise indoor positioning method that uses low power, but
very high time resolution signals to achieve centimetre to level precision and decimetre level accuracy
ranging allowing for very precise indoor positioning in both one-way and two-way modes [23]. The
difficulty with UWB is that while the cost of UWB radios has decreased significantly in the past decade,
they are still not commonly found in consumer electronics. But in the near term only a small fraction
of mobile phones will be equipped with UWB ranging radios.
3. Proposed System Model
We propose to test peer-to-peer line of sight ranging between mobile users. In this work-in-progress
paper, out goal is to assess if UWB can provide sufficiently precise peer-to-peer ranges to determine a
BLE RSSI path-loss exponent along the same path. If successful, we will then investigate more complex
RSSI to range models and methods, including using large quantities of UWB data to train artificial
networks to convert BLE RSSI values to ranges. The assumption is that a small number of UWB-
equipped users will be able to provide enough training data to enable BLE RSSI-based ranging for most
of the users who do not have UWB radios.
The project uses two DWM1001-DEV (Decawave, Dublin, Ireland) developmental kits [24]. The
development kit includes both an nRF52832 BLE radio and a DW1000 UWB module and thus can be
used as both a BLE source and a UWB transceiver. A separate nRF52840 development kit (Nordic
semiconductor, Norway) [25], co-located with the second DWM1001-DEV, is used as a BLE receiver,
logging RSSI values at 50ms rate, and the two DWM1001-DEVs perform double sided two-way
ranging at 100ms rate. To explore the performance of the proposed system, an experiment is performed
in a narrow corridor (2.4 m wide by 10 m in length) located on the third floor in the CCIT building of
the University of Calgary illustrated in Figure 1
Wall
0.5 m
2.4 m
1m Transmitter
Measurement Points
Wall
[0,0]
10 m
Figure 1: Environmental Scenario (not to scale)
4. Preliminary Results
Measurements were collected in static mode over a number of distances ranging from 1 to 5 metres.
Even in static mode, the raw BLE RSSI measurements show significant fluctuations. In contrast, the
UWB range shows no fluctuation and gives precise measurements of the line-of-sight range. Figure 2
shows the raw RSSI values observed over a 1 m range as well as a filtered version of the Channel 37
RSSI values and a histogram of each channel. Figure 3 shows the distribution of UWB range
measurements at 1 metre and 2-metre distance. We log both the BLE RSSI and UWB range in our
experiments together. Then, we consider the samples of RSSI
Figure 2 (a): Raw received signal strength indicator (RSSI) values, (b) Kalman Filter output of
Channel 37 (c) Histogram plot of raw RSSI at a fixed place showing the spread of RSSI.
and range for all the matched time instants. Subsequently, a Kalman filter is applied to filter out each
BLE channel's raw RSSI values to remove outliers and obtain more stable RSSI data. We measure RSSI
values from 1 metre to 5-metre distance (logging data for more than 15 minutes in each location).
It is observed that the RSSI values observed over more than 5 metres often show constructive
interference and a change in the path-loss exponent and for this reason we conducted the experiment up
to 5 m. In addition, the variability in the RSSI values increases with distance. A simple line fit model
was applied to the RSSI values using both a true (laser range finder) and UWB distance for the x-axis.
The resulting models were then evaluated for each RSSI measurement to determine a residual range
error for each RSSI measurement. The linear models, shown in Figure 4, do not fit particularly well as
can be seen from the histograms of the corresponding RSSI-based range errors shown in Figure 6. This
performance can be improved if the line fit is limited to distances of 3 metres or less as shown in Figure
5 and the corresponding RSSI range residuals better than 15 cm shown in Figure 7. In both cases, the
use of true (laser range finder) ranges results in line fits that lead to slightly better RSSI-based ranges,
however the UWB-based line fits are very similar to those obtained with the true ranges, demonstrating
the feasibility of using UWB to gather training data for RSSI path-loss models.
(a) (b)
Figure 3: UWB Range distribution at (a) 1-metre and (b) 2-metre LOS distance in complex indoor
environment
Table 1: Channel 38 filtered RSSI and UWB measurements at LOS in complex indoor environment.
Actual Distance (m) Lowest RSSI Value (dBm) Highest RSSI Value(dBm) Average UWB Distance (m)
1 -41.4 -40.8 0.99
1.5 -44.2 -43.2 1.46
2 -50.02 -49.3 2.01
2.5 -58 -54.1 2.48
3 -61.2 -58.4 3
4 -55.5 -54 4.05
5 -47.8 -46.6 5.03
Figure 4: Distance vs RSSI Line fit by plotting cluster of RSSI points up to 5m using
true and UWB distance.
Figure 5: Distance vs RSSI Line fit by plotting cluster of RSSI points up to 3m using true and
UWB distance.
(b)
(a)
(c) (d)
(e) (f)
Figure 6: Residual error of distance estimation using a 6-point line fit model with true and UWB
distance at a) 1m, b) 1.5m, c) 2m, d) 2.5m, e) 3m, and f ) 4m respectively
(a) (b)
(d)
(c)
(e)
Figure 7: Residual error of distance estimation using a 5-point line fit model with true and UWB
distance at a) 1m, b) 1.5m, c) 2m, d) 2.5m, e) 3m respectively.
5. Conclusion and Future Plans
This work demonstrated that UWB can provide precise ranges in order to fit a line to RSSI values.
The log-distance model for distance estimation depends very highly on the environment. The BLE
advertising channels have different transmission power levels due to carrier frequency, channel gain,
multipath, and fading. The preliminary experiments show that a simple line fit model is suitable for
estimating a short-range from RSSI values with an error less than 15 cm is feasible up to 3 metress. The
error in distance estimation increases after 3 metres as the impact of constructive interference begins to
change the path-loss exponent.
The next step in this work will be to collect more and varied training data, in the form of BLE RSSI
and corresponding UWB ranges, and then compare fitting more and multiple lines to determine
difference path loss exponents for many different environments with using artificial intelligence to
develop implicit models for converting RSSI into range for each of these environments.
More investigation is required to understand the nature of the environments and the amount of
training data needed for AI to learn patterns from the data. The applicability of models developed in
one environment to data gathered in different environments also needs to be assessed. Finally, we hope
to understand if BLE RSSI from low-cost general mobile users can be used to determine the precise
proximity between two BLE-enabled devices in any future critical pandemic situations.
6. Acknowledgements
This work was partially funded by the Natural Sciences and Engineering Research Council of
Canada (NSERC) CREATE Program on Multi-sensor Systems for Navigation and Mapping,[funding
reference number 495568-2017].
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