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RFID Tracking using Robust Support Vector Regression and Kalman Filter in Safety
Management of Industrial Plants
Jian Chai1, Changzhi Wu
2, Hung-Lin Chi
3, and Xiangyu Wang
4
1) Ph.D. Candidate, Australasian Joint Research Centre for Building Information Modelling, Curtin University, Perth, WA,
Australia. Email: [email protected]
2) Ph.D., Senior Research Fellow, Australasian Joint Research Centre for Building Information Modelling, Curtin University,
Perth, WA, Australia. Email: [email protected]
3) Ph.D., Research Fellow, Australasian Joint Research Centre for Building Information Modelling, Curtin University, Perth,
WA, Australia. Email: [email protected]
4) Ph.D., Prof., Australasian Joint Research Centre for Building Information Modelling, Curtin University, Perth, WA,
Australia. Email: [email protected]
Abstract:
Site operations usually come with potential safety issues. An effective monitoring strategy for operations is
important to identify risk in advance and further prevent possible accidents. Regarding the status
monitoring among material, equipment and personnel during site operations, much work is conducted on
localization and tracking using RFID (Radio Frequency Identification) technology. However, existing
tracking methods suffer from low accuracy and instability, due to severe interference in large areas with
many metal structures. To improve RFID tracking performance in industrial sites, a RFID tracking method
integrating MSVR (Multidimensional Support Vector Regression) and Kalman filter is developed in this
study. A safety management system is also developed based on the RFID tracking method as a feasible
application. Experiments are conducted on a real Lignified Natural Gas (LNG) training facility with long
range active RFID system to evaluate performance of this approach. Results demonstrated the effectiveness
and stability of the proposed approach with severe noise and outliers. It is feasible to adopt the proposed
approach which satisfies intrinsically-safe regulations for safety management in current practice.
Keywords: RFID tracking, support vector regression, Kalman filter, safety management.
1. INTRODUCTION
RFID technologies have been proved as effective tools for prompting the management in industrial and
construction sites (Gandino et al., 2009). In past decades, RFID has been applied in various tasks of construction,
such as progress tracking, resource monitoring and safety management. Integration and bi-directional
coordination between virtual model and the physical construction can promote efficiency during the whole life
cycle of a project (Akanmu et al., 2014). RFID is one of the potential technologies to integrate virtual models
and the physical construction via real time localization and information communication.
Many RFID based positioning methods have been proposed during last decades, such as K Nearest Neighbors
(KNN) (Motamedi et al., 2013), Weighted Centroid Localization (WCL) (Laurendeau & Barbeau, 2010) and
fingerprinting based approaches. Some researchers also adopt Particle Filter (PF) or Kalman Filter (KF) to
enhance robustness and accuracy of object tracking (Yang & Wu, 2014). Those approaches perform well in close
range localization with dense reference tags; however they suffer large errors and low robustness when applied
in large complex environments. Besides, deployment of dense reference tags in large areas will significantly
increase cost.
The construction industry suffers from a significant amount of risks, and RFID tracking technologies have been
used to alert workers to danger (Arslan et al., 2014; Sole et al., 2013). Those hazard prevention methods depend
on real time localization using RFID technology and are referred as location-based safety management (Lee et al.,
2011). However, because of complexity and rapid changes on construction sites, one of the key factors
influencing location based safety management is the performance of RFID tracking methods (Cheng et al.,
2011).
The full potential of RFID technology in construction is not realized. One of the reasons is due to the robustness
and accuracy of RFID localization in complex environments. The objective of this study is to develop a robust
RFID tracking method and to promote location-based safety management by integrating building information
model (BIM) and physical construction site. This will contribute to more efficient information communication
between construction managers and site workers, potentially improving productivity and reducing risks in
construction projects.
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Contributions of this study are as follows:
1. A robust RFID tracking method is developed based on multidimensional Support Vector Machine
(MSVR) and Kalman filter;
2. A location-based safety management system is developed by real time integrating building information
model with physical site using the developed RFID tracking method;
3. A case study in a Lignified Natural Gas (LNG) training site is conducted and validates the developed
system.
2. RFID TRACKING APPROACH
MSVR Parameter selection
RSS and Positions of
reference tags
Localization using robust MSVR
Training robust MSVRReal time measurements
of reference tags
System Initialization
Real time tracking
Parameters of MSVR
Real time measurements of
moving tags
start
Motion model
State Update
Measurements
Current state
Update
Measurement model
Kalman Filter
Real time tracking
Positioning
Positions end
State prediction
Pre-processing
Figure 1. Overview of the developed RFID tracking approach
A RFID tracking system consists of three main components: RFID tags, readers and a tracking server. In our
study, active tags are used because of their long communication distance and Received Signal Strength (RSS) of
a tag is measured by readers to estimated position of the tag.
The overall process of our RFID tracking approach is depicted in Figure 1. The approach first positions a tag
using a multidimensional SVR method. Parameters of MSVR are selected according to measurements of
reference tags (pre-installed tags with known positions) during an initialization step. Kalman filter process is
then deployed to track continuous positions of a tag.
The principle of our positioning approach is to determine position of a new observed tag using training
information of reference tags. Here, reference tags mean tags fixed at known positions and are used to assist in
positioning. Considering a RFID network of tags 𝑇 = {𝑇𝑖 , 𝑖 = 1 … 𝑛} and readers 𝑅 = {𝑅𝑗 , 𝑗 = 1 … 𝑚}, where
n is the number of reference tags and m is the number of RFID readers, position of each tag is denoted as
𝒚𝑖 = [𝑦1 𝑦2]𝑇 , 𝑖 = 1 … 𝑛 and RSS measurement vector of tag 𝑇𝑖 at time t is denoted as
𝒙𝑖 = [𝑥𝑖1… 𝑥𝑖𝑚 ]𝑇where 𝑥𝑖𝑗 is the RSSI measurement of tag 𝑇𝑖 by reader 𝑅𝑗. Given a new observed tag
𝑇𝑢 with RSS measurement 𝒙𝑢(𝑡) at time t, the goal is to estimate the tag's position 𝒚𝑢(𝑡) from 𝒙𝑢(𝑡).
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However, due to influences of environments, accuracy of distance derived from an RSS measurement cannot
satisfy accuracy requirement of location. Instead of deriving distances from RSS measurements, we directly train
a global model based on reference tags. Based on the assumption that a tag's position can be determined based on
its RSS measurements, the problem is formulated as a non-linear regression problem. That is to regress a
non-linear function which maps the RSS measurements of a tag to its location.
𝒚𝑖 = 𝑓(𝒙𝑖) = 𝐖∅(𝒙𝑖) + b (1)
where ∅(𝒙𝑖) is a nonlinear function mapping observations to high dimensional feature spaces, 𝐖 is
a weight matrix and 𝐛 is a bias vector.
At any time t, a function 𝑓(𝒙𝑖) is trained based on positions and RSS measurements of reference tags. Then it is
used to estimate location of a dynamic tag.
An anomaly detection step is first carried out to correct outliers in measurements of reference tags. This step
compares a tag’s measurements with a theoretical one estimated by weighting its neighboring tags’
measurements. If their deviation is above a threshold, the measurement will be identified as anomaly and
corrected according to the estimation. The effects of anomaly detection process are shown in Figure 2 and it
shows that the step can effectively reduce fluctuations of RFID signal.
Figure 2. RSS measurements before (left) and after (right) anomaly detection
A robust multi-dimensional SVR approach is then adopted to estimate the function (1) and enhance robustness of
the localization algorithm against outliers. As the approach described in a previous work (Chuang et al., 2002),
the basic idea of this method is to combine robust learning concept with SVR method. First, initial weights and
network structure are estimated with SVR method. Then, a robust learning algorithm is employed to adjust those
initial weights. The function (1) is estimated by solving the following optimization problem (2) using
multidimensional SVR (Pérez-Cruz et al., 2004).
min𝑊,𝑏,𝜉𝑖 (‖𝐖‖2 + 𝐶 ∑ 𝜉𝑖)𝑛𝑖=1
subject to {‖𝒚𝑖 − 𝐖∅(𝒙𝑖) − b‖ ≤ 𝜉 + 𝜉𝑖
𝜉𝑖 ≥ 0
(2)
where C is the regularization parameter and ξi is the nonnegative slack variables to deal with
permitted errors.
The Kalman filter is integrated to improve the robustness of the tracking approach. It can estimate recursively
optimal states in a two-step process. It first predicts position of a tag from its previous state based on a prediction
model, and then moderates the estimation based on current measurements and a linear measurement model
(Humpherys et al. 2012). Since its combination of current measurements, previous state and characteristics of
recursive, this algorithm can real time estimate current states more precisely.
To model the motion of moving tags, the second order kinematics model is used here (Bar-Shalom et al. 2004).
The velocity of this model is constant except an acceleration noise term. Besides the prediction model, a linear
measurement model is also required by Kalman filter. However, in our approach position is estimated from the
RSS measurements by MSVR method and there is no explicit relationship between RSS measurements and
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positions. Thus original RSS measurements cannot be directly used by Kalman filter. Motivated by the WLAN
tracking approach in Kushki’s work (Kushki 2008), the position 𝒚𝑖 estimated by MSVR method is used as a
synthetic measurement to replace original RSS measurements. Then, the synthetic measurements can be linearly
related to the state vector of a tag as (3).
𝒚𝑖 = 𝑯𝒙(𝑘) + 𝒗(𝑘) (3)
where 𝒚𝑖 is the estimated position, 𝒙(k) is the state vector of a tag, and 𝒗(k) is white Gaussian
noise, and
𝑯 = [1 00 0
0 01 0
] (4)
3. Safety Management
The proposed safety management system can be seen in Figure 3. It connects real time location of resources with
BIM using RFID technology and the Internet. This makes real time visualization and monitoring of a
construction site in BIM, helping a construction manager realize situations on site. More importantly, the
integration with BIM enables further analysis of site operations such as to promote construction management
tasks. Clash prediction based on real time locations can avoid risks of collisions between workers and machines.
Location based access control and monitoring can also address safety issues regarding hazardous areas.
Equipment monitoring can give information on construction progress. Recognition of resource moving patterns
enhances knowledge of construction operations, leading to better work planning.
BIM
Active RFID
Tracking
System
Internet
(TCP/IP)
Moving
Personnel and
Equipment
Safety
Monitoring
System
Mobile Devices
Radio
Signals
Calculated
Positions Calculated
Positions
Geometrical/ Safety
Information
Detected Safety
Issues
Detected Safety
IssuesIssues Notification
Figure 3. Overview of the developed safety management system
The positions of onsite resources are tracked by the proposed RFID tracking system, including a network of
readers, tags attached to each object intended to be tracked, and a tracking server communicating with readers
via Wireless Local Area Network (WLAN). A safety management system is developed by analyzing those
positions together with BIM. BIM serves as an information center containing information regarding safety such
as hazardous areas, critical working conditions. Based on past cases, information regarding activities of high
risks can also be retrieved from BIM. Mobile devices are incorporated to convey warnings and solutions from
the safety management system to onsite workers.
To avoid potential hazards, a collision prediction model is also developed to estimate possible dangers of
moving items. Given timely state of an item, its following track can be estimated using a motion model. The
prediction model will forecast possible safety issues induced by motion of an item. For instance, a moving item
is likely to crash another one or run towards a hazardous area. If any risk is expected, an alert will be triggered
and sent to involved site workers equipped with mobile devices. Let us define 𝑥(𝑡)as the state vector of a tag.
𝑥(𝑡) = [𝑝𝑥(𝑡) 𝑣𝑥(𝑡) 𝑝𝑦(𝑡) 𝑣𝑦(𝑡)]𝑇 (5)
where 𝑝𝑥(𝑡), 𝑝𝑦(𝑡) are position coordinates and 𝑣𝑥(𝑡), 𝑣𝑦(𝑡) are velocity.
According to the second order kinematics model, at time 𝑡 + ∆, the state vector of a tag is as Equation (6).
𝑥(𝑡 + ∆) = 𝐹𝑥(𝑡) + 𝑤(𝑡)
𝐹 = [
1 ∆ 0 00 1 0 000
00
10
∆1
] (6)
where 𝑤(𝑡) is white Gaussian acceleration noise.
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With Equation (6), we can predict motion of an object and judge whether two tags will run into each other or
towards a hazardous area in terms of current motions. For instance, a worker shown in Figure 4, who is
approaching the processing train, will be sent an alert about potential safety issue. Equipped with a mobile
device, the person will not only be noticed the issue but also be suggested how to avoid the potential issue, such
as changing moving direction or wearing protective equipment.
Pumps and Pipes
Process Train
Work Shop
Dehydration
Module Vessel
Road
Process Room
ReaderReader
Reader ReaderTank
Road
Fence
Container
Container
Drilling
SamplesVessel
Cable
Tray
Figure 4. Buffering zone of a moving item
If possible collisions may happen, the integrated system will send an alert to the manager and to the involved
workers with a mobile device and WLAN abilities. With RFID tracking approach, motion states of each item
including position and velocity are timely updated. Each item is also allocated a buffering zone centered at its
location, the size of which is determined based on positioning accuracy and size of an item. As shown in Figure
4, a circle around each moving item, such as a worker or a forklift machine, indicates size of its buffering zone.
Apparently, the buffering size of a forklift machine is larger than that of a worker in terms of its speed and size.
The buffering zone ensures safety by taking account of uncertainty on sites. A mobile device is equipped to
every onsite object, which reminds of potential danger and suggests possible solutions. Depends on availability,
different types mobile devices can be allocated to different objects. It can be just an interphone telling that there
is a danger ahead. It can also be an Augmented Reality (AR) device intuitively displaying related information.
For a machine, a monitor may be deployed to warn possible risks using Virtual Reality (VR) technology.
3. CASE STUDY
A system prototype is developed to link BIM with RFID by the developed tracking method and validated by a
case study in a LNG training site. The system is developed based on Autodesk Navisworks. A RFID tracking
system is deployed on the site consisting four readers and a series of active tags. The readers are encapsulated in
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enclosures (as Figure 5(a)) with antennas operating at 433MHz radio frequency. An active RFID tag as those
shown in Figure 5(b), which operates at the same frequency and stores user’s identification, is attached to a
worker onsite. When the worker is moving around the site, the readers receive RF signals from the tag and by
through triangulation process the location will be identified. Thus, position information will be associated with
an avatar through plugin and visualized real time within Navisworks platform.
Figure 5. RFID equipment deployed in the experiment: (a) Reader and (b) Tags
Results of the RFID tracking experiment are shown in Figure 6. As can be seen, our method can efficiently track
a moving object on a LNG site and its accuracy is around 1 meters. Besides, compared to MSVR only, errors
after using Kalman Filter are much smaller and fluctuations are much less, which demonstrates the robustness of
our tracking algorithm. In the experiment, we utilized reference tags to calibrate the received RF signals of the
target. The totally number of reference tag is 165 and they formed 11 by 15 grids surrounding the four readers.
Figure 6. Results of the proposed RFID tracking approach: trajectories (left) and errors (right) of RFID tracking
before and after Kalman filtering
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A safety management plugin is developed to real time predict potential risks according to motion on site. For
example, site managers can retrieve the status of the site, compare with the authorized work permits and
determine potential safety issues to be aware of for site crews. The prediction results will be not only visualized
on Navisworks but also transmitted to onsite crews through WLAN and mobile devices. In our experiment, a
smart phone is adopted as a mobile device. When receiving a safety alert, the phone will sound an alarm and an
arrow will be also overlaid on the camera view of the phone, showing the direction on which a risk is coming. A
tip will also pop up on the screen to suggest actions to prevent the danger.
5. CONCLUSIONS
In this work, a robust RFID tracking method is developed. This method integrates robust multidimensional
support vector regression with Kalman filter to improve the robustness against noise and outliers in an industrial
site. A safety management system based on the RFID tracking method is also proposed by linking BIM and the
physical site. This system can address risks related to locations on site. A prototype is implemented and a case
study is conducted on a LNG training site to evaluate the developed system. Results proved the robustness of
RFID tracking method and effectiveness of the safety management system. Future work will be focused on
integrating other sensing and tracking technologies to address limitations of RFID merely and take account of
other factors impacting construction safety.
ACKNOWLEDGMENTS
This research was undertaken with the benefit of a grant from Australian Research Council Linkage Program
(Grant No. LP130100451).
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