tao-yun2018

Accepted manuscript to appear in IJPRAI
Accepted Manuscript
International Journal of Pattern Recognition and Artificial Intelli-
gence
Article Title: Amp-Phi: a CSI-based Indoor Positioning System
Author(s): Zhou Tao-Yun, Lian Bao-Wang, Zhang Yi, Liu Sen
DOI: 10.1142/S0218001418580053
Received: 28 November 2017
Accepted: 07 February 2018
To be cited as: Zhou Tao-Yun et al., Amp-Phi: a CSI-based Indoor Positioning System,
International Journal of Pattern Recognition and Artificial Intelligence,
doi: 10.1142/S0218001418580053
Link to final version: https://doi.org/10.1142/S0218001418580053
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Amp-Phi: a CSI-based Indoor Positioning System
Zhou Tao-Yun1,2 Lian Bao-wang1,* Zhang Yi1 ,Liu Sen1
1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
2. School of Information, Hunan University of Humanities, Science and Technology, Loudi, China
*. Corresponding author
Abstract：With a rapid growth in the demand of Location Based Services (LBS) in indoor
environments, localizations based on fingerprinting have attracted significant interests due to their
convenience. Until to now, most of such methods are based on Received Signal Strength Indicator
(RSSI), which is vulnerable to Non-Line-of-Sight (NLOS). In order to realize a high-precision indoor
positioning, we propose a CSI-based Amp-Phi indoor positioning system which exploits the amplitude
and phase information of Channel State Information (CSI) at the same time to establish a fingerprinting
database. Firstly, according to the characteristics of the raw CSI information collected at different
positions under different environments, we build a NLOS mitigation model and a phase error
mitigation model respectively to process the amplitude and phase of CSI. Secondly, we analyze the
statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After
being processed with the models, the CSI features can be used to distinguish different positions clearly,
which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build
a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s
position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in
different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher
than that of PhaseFi, Horus and ML systems under the same condition. It can be used in many
scenarios, such as the localization of robots in our daily life, doctors or patients in the hospital, people
localization in large supermarket or museum and so on.
Key words： Channel State Information (CSI), Indoor Positioning, KNN Matching Algorithm,
Received Signal Strength Indicator (RSSI), Non-Line-of-Sight (NLOS)
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Accepted manuscript to appear in IJPRAI
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1. Introduction
Indoor positioning technology based on smart phones has many application scenarios since people
stay inside buildings more than 80% of their daily life [1,2]. WLAN-based indoor location
fingerprinting has been attractive owing to the advantages of open access and high accuracy. A large
number of companies including both domestic and international based have devoted themselves to the
research of indoor positioning and have already produced many positioning systems and solutions.
Apple introduced i-Beacon in 2013 and began to use Bluetooth for indoor positioning, it opened the
Core-Location API in IOS 8 system at the Apple Worldwide Developers Conference (WWDC) in 2014,
which could allow the developers to capture the user’s precise position in indoors[3,4]. Currently,
Google has extended Google Maps to indoors, and has mapped museums, airports and shopping malls
for at least 17 countries, such as the Tai Po Super City in Hong Kong [5,7]. There are also many teams
in China who are engaged in the research of indoor positioning products, such as Local-Sense indoor
positioning system, High German Maps and Smart Maps and so on[8].
Most fingerprinting-based systems so far rely on RSSI, which can be easily measured at the
receiver with some commercial WLAN equipment. The RSSI based methods [9-13] calculate the
distance between two nodes and locate the unknown target with at least three known nodes, although
they have a good performance in outdoors, the position errors in indoors are large due to the multipath
effects. Such methods have three main disadvantages: First, due to the small-scale shadow fading
caused by multipath effects in indoor environments, the RSSI values no longer increase monotonously
with the propagation distance, which limits the ranging accuracy. Second, due to the NLOS, RSSI
values at the same position will change greatly over time, and bring big errors even for a stationary
device, the fluctuation of a typical laboratory environment is about 5dB in one minute [14-15]. Third,
RSSI values only reflect the superimposed amplitude of multipath, but ignore the rich multipath
information of subcarriers in an orthogonal frequency-division multiplexing (OFDM) system, so they
can’t distinguish the multiple propagation paths one by one. Hence it is of critical importance to search
for a much more stable and reliable information used to build a fingerprinting other than the RSSI.
Recently, the IEEE 802.11 standardization is found to include rich channel properties in the form
of channel state information based on OFDM technology. So more and more researchers concentrate on
using the CSI to resolve the multipath effects existed in RSSI. With the availability of CSI from the
physical layer, the Wi-Fi indoor positioning systems have gradually shifted from RSSI to CSI [15]. On
one hand, instead of measuring the superimposed amplitude response of all subcarriers, it measures the
frequency response of multiple subcarriers from one packet simultaneously, which can finely describe
the frequency selectivity of the channel. On the other hand, by measuring the amplitude and phase of
each subcarrier, it extends the single-valued RSSI to the frequency domain, and provides much richer
and finer CSI in frequency domain [16]. By building some efficient models to preprocess the amplitude
and phase of the raw CSI, the multipath effects can be mitigated to a certain extent, so CSI is much
more stable and reliable than RSSI in indoor positioning [17].
In [18] a Fine-grained Indoor Fingerprinting System (FIFS) is proposed, where the CSI is the
weighted average CSI values of multiple antennas. In [19] a PinLoc system is proposed, where it
considers 1×1 m2 spots for training data and exploits the CSI information. FILA [20] proposes a new
ranging model based on CSI expecting to reduce the signal propagation error existed in the traditional
indoor positioning, and determines the final position with trilateral positioning method. However, the
model is sensitive to environmental changes and needs to train the parameters in a specific indoor
scenario to measure the distance accurately. In addition, ranges between the target and APs can’t be
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measured in parallel, thus it can’t realize real-time position in WIFI scenario. Experimental results
show that the 80% accuracy is about 2 m and 90% accuracy is about 3 m, with three APs being taken
part in. CUPID [21] transforms the CSI to time domain and measures the range by considering the first
bar of the energy distribution graph to be the line-of-sight. This will result in large errors in real
environment, because there exists an uncertain time lag within the shortest delay. Experimental results
show that the 80% accuracy is about 3m and 90% accuracy is about 4m, with five APs being taken part
in. In [22-23], a DeepFi system is proposed, in which the fingerprinting is the weights of a network
trained with a greedy learning algorithm in the offline training stage, and a probabilistic method is
used to locate the target’s position in the online positioning stage. However, the phase information of
CSI is not used for the building of fingerprinting. In [24-25], a PhaseFi system is proposed where the
phase information is used to build the fingerprinting database and a greedy learning algorithm is used
to train the weights, however, it doesn’t use the amplitude information.
In this paper, we propose a fingerprinting-based Amp-Phi indoor positioning system, in which the
amplitude and phase information are used at the same time to build the fingerprinting database. The
basic ideas can be concluded as: Firstly, according to the characteristics of the raw CSI information
collected at different positions under different environments, we build a NLOS mitigation model and
phase error mitigation model respectively to process the amplitude and phase of CSI. Secondly, we
analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and
variance. After being processed with the models, the CSI features can be used to distinguish different
positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting.
Finally, we build a fingerprinting database based on the amplitude and phase features of CSI and
realize the target’s localization with the K-Nearest Neighbor (KNN) matching algorithm. We perform
some experiments in two typical scenarios: one is the complex computer laboratory where exist a lot of
NLOS due to the block of furniture and computers, the other is the relative simple living room, there is
little NLOS for no large things exist. Both in the two environments, we compare the Amp-Phi system
with the newest method PhaseFi proposed in 2016 [25], the most classical methods Horus [26] and ML
[27] proposed in 2005. In the laboratory, the position accuracy of the four system is 4m,4.2m,5.4m and
6m respectively, and in the living room, the position accuracy of the four system is 1.7m,1.8m,2.5m
and 3.7m respectively. That is to say, among the four systems, Amp-Phi performs best and the PhaseFi
system takes the second place, this is because both of them use CSI which has much more fine-grained
subcarriers information than RSSI, for Amp-Phi fuse the amplitude and phase information together, its
position accuracy is better than that of PhaseFi which only use the phase information.
The rest of the paper is organized as follows. In section 2, we collect the raw CSI information and
build the CSI range model. In Section 3, we build a NLOS mitigation model and a phase error
mitigation model respectively to process the amplitude and phase of CSI, and analyze the
characteristics of CSI. In section 4, we build a fingerprinting database based on amplitude and phase at
the same time and realize to locate the target’s position in indoors, and do some experiments in two
typical scenarios. Finally, in section5 we conclude our work.
2 Collection of CSI Data and Establishment of Range Model
2.1 Collection of CSI Data
In the wireless communication protocol, CSI belongs to the physical layer information and can’t
be obtained directly. TNS-CSI Tool, which is an integrated installation tool based on Linux 802.11n
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can be used to extract CSI information and store them in real-time. The specific steps of obtaining the
CSI are as follows:
(1) Replace the laptop's wireless network card with Intel 5300 which have three antennas. For
simplicity and without loss of generality, we just analyze the data collected from antenna A and antenna
B, for the analysis methods of the three antennas are similar.
(2) Install the Ubuntu11.04 operating system and replace the system software source with 11.04
Version for laptop.
(3) Compile and install the TNS-CSI Tool, modify the firmware and NIC driver and recompile the
kernel options.
When all the essential software has been installed, the CSI information is recorded in the xx.dat
file and can be read by the TNS-CSI Tool. The experimental devices required to acquire CSI data is
shown in Fig.1.
Fig.1 The experimental devices required to acquire CSI data
The CSI is stored in a three-dimensional matrix with a size of
30
XX
NR NT
,
X
NR
and
X
NT
is the number of receive and transmit antennas respectively, 30 in the third dimension
represents 30 OFDM subcarriers (in the following Tables it is abbreviated as Sub). There are 56
subcarriers in the CSI but only 30 subcarriers can be used when the bandwidth is 20 MHz,
measurement information of CSI can be seen in Table.1.
Table.1 Measurement Information of CSI
Bandwidth
Grouping
Number of Sub
Serial Number of Sub
20MHz
2
30
-28,-26,-24,-22,-20,-18,-16,-14,-12,-10,-8,-6,-4,-2,-1, 1, 3, 5, 7, 9, 11,
13, 15, 17, 19, 21, 23, 25, 27, 28
40MHz
4
30
-58,-54,-50,-46,-42,-38,-34,-30,-26,-22,-18,-14,-10,-6,-2, 2, 6, 10, 14,
18, 22, 26, 30, 34,38, 42, 46, 50, 54, 58
2.2 Establishment of Range Model based on CSI
CSI is compatible with IEEE 802.11a/g/n protocols, it can be acquired with a common wireless
network card and an open source firmware [4]. CSI includes the channel properties of a communication
link, describes how a signal propagates from the transmitter to the receiver and reveals the combined
effects of the multipath, such as scattering, fading, and power decay with distance.
In a narrowband flat-fading channel, the OFDM system in frequency domain can be modeled as:
Y HX n
(1)
Where
Y
is the channel output vector which is always distorted by the multipath effects,
X
is
the input vector which we need, namely the CSI information.
H
is the channel state information matrix
of the subcarriers and
n
is the additive white Gaussian noise (AWGN). Usually
n
subjects to a normal
distribution whose mean is zero and variance is
2
, namely,
2
~ (0, )nN
.
In order to extract
X
from
Y
successfully, it is necessary to estimate the channel state information
matrix
H
, the estimation of
H
can be described as:
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