=Paper=
{{Paper
|id=Vol-3248/paper16
|storemode=property
|title=UDOKS Project: Development of a Pedestrian Navigation System With Multiple Integrated Sensors
|pdfUrl=https://ceur-ws.org/Vol-3248/paper16.pdf
|volume=Vol-3248
|authors=Serkan Zobar,Mehmet Çiydem
|dblpUrl=https://dblp.org/rec/conf/ipin/ZobarC22
}}
==UDOKS Project: Development of a Pedestrian Navigation System With Multiple Integrated Sensors==
https://ceur-ws.org/Vol-3248/paper16.pdf
UDOKS Project: Development of a Pedestrian Navigation System
With Multiple Integrated Sensors
Serkan Zobar 1,2 and Mehmet Çiydem 2
1
Defence Systems Technologies Business Sector, ASELSAN Inc., 06200 Ankara, Turkey
2
Department of Electrical and Electronics Engineering, Gazi University, 06570 Ankara, Turkey
Abstract
In both civilian and military areas, accurate and robust navigation systems have become a
critical tool for positioning and tracking capabilities of pedestrians under Global Navigation
Satellite Systems (GNSS)-denied conditions, e.g., underground workers, dismounted soldiers
on battlefield, and first responders within a building. In order to navigate through these harsh
environments, pedestrian navigation systems (PNSs) with multiple integrated sensors are
indispensable. In the research project which is funded by ASELSAN and entitled "Positioning
in GNSS Denied Environments (labeled as UDOKS)", we are working on the development of
a PNS integrating inertial sensors (i.e., gyroscopes and accelerometers), Ultra-Wideband
(UWB) ranging sensors, and aiding sensors such as magnetometers and a barometer.
Furthermore, a simulation environment dedicated to PNSs with multiple integrated sensors is
being implemented as a part of UDOKS project. The simulation environment will allow the
user to compare several pedestrian navigation algorithms which are based on different types of
sensor technologies and different levels of sensor errors. Moreover, the simulation environment
will be used as a means to test and evaluate new strategies and approaches on pedestrian
navigation during algorithm and configuration design phase. The simulation environment will
have an open and modular architecture so that it will be capable of inserting and integrating
complementary navigation algorithms and sensor technologies. This open and modular
architecture of the simulation environment will provide a huge potential benefit to design PNSs
especially for the needs in various GNSS-denied applications and usage scenarios.
This Work-in-Progress (WiP) paper provides a high-level description of the PNS being
developed and a general outline of the simulation environment being implemented in UDOKS
project. Furthermore, preliminary indoor performance results of the pure inertial sensors-based
PNS (i.e., utilizing only gyroscope and accelerometer measurements) developed as a single
navigation system are given.
Keywords 1
Pedestrian navigation, sensor integration, open and modular architecture
1. Introduction
Over the last three decades, Global Navigation Satellite Systems (GNSS) have become the main
positioning and navigation tool for most applications in both civilian and military areas. However,
GNSS may not be accessible or practicable in some environments such as indoors, below the ground
level, and dense urban areas [1]. To obtain positioning and navigation solutions in such GNSS-denied
environments, integration of multiple sensors is inevitable. Of course, the selection of appropriate
sensors to integrate depends mainly on the requirements of the application and the knowledge of the
usage scenario. Specifically, body-worn pedestrian navigation systems (PNSs) require low size, weight,
power and cost (SWaP-C) sensors while the performance accuracy still meets the application's
IPIN 2022 WiP Proceedings, September 5–7, 2022, Beijing, China
EMAIL: szobar@aselsan.com.tr (S. Zobar); mehmetciydem@gazi.edu.tr (M. Çiydem)
ORCID: 0000-0001-5731-7955 (S. Zobar); 0000-0001-9164-8491 (M. Çiydem)
©️ 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)
requirements. Therefore, the development of a PNS can be made possible by intelligent methods and
algorithms with the ability to address the sensor errors and to reduce their adverse effects on the
navigation performance.
1.1. Related works
Pedestrian navigation technologies used for achieving reliable positioning in GNSS-denied
environments can be classified into two categories [2]: infrastructure-free and infrastructure-based
technologies.
The infrastructure-free technologies depend only on inbuilt sensors. They do not rely on any external
infrastructure pre-installed in the environment. Among these technologies, inertial sensors, i.e.,
gyroscopes and accelerometers, are the most common ones [3]. Using the motion information measured
by body-mounted inertial sensors, the pedestrian’s position relative to the starting point can be estimated
[4-9]. The latest advances especially in Micro Electro-Mechanical System (MEMS)-based inertial
sensor technology have brought huge profits to develop PNSs utilizing SWaP-C sensors. A big
drawback of inertial sensors-based PNSs is that the positioning error unavoidably increases with time
due to noise and biases on the sensor measurements [10]. The inertial sensors of a PNS can be assisted
by some other sensors such as magnetometers [11], barometers [12], pressure gauges [13], ultrasonic
sensors [14] and radars [15] in order to enhance the accuracy of the position estimation. However, this
aiding makes the PNS more complex and more delicate against some environmental conditions (e.g.,
magnetometer aiding fails in environments with serious magnetic field disturbances). As another
infrastructure-free technology, vision-based sensors (cameras) have become an attractive alternative for
pedestrian positioning and navigation in recent years [16]. Vision-based navigation systems use
cameras to apply visual odometry to estimate the trajectory of the camera using only a stream of images
[17].
The infrastructure-based technologies rely on devices or facilities installed in the environment in
advance and include Radio Frequency Identification (RFID) [18], Wireless Fidelity (Wi-Fi) [19], Ultra
Wideband (UWB) [20], and Bluetooth Low Energy (BLE) [21] technologies. These technologies
provide distance- or angle-related measurements for position estimation such as Received Signal
Strength Indicator (RSSI), Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Angle of
Arrival (AoA) [22]. Pre-installing the infrastructure for these technologies is a costly task. In addition
to the high cost, in case of infrastructure failure, the positioning or navigation systems based solely on
these technologies fail.
In order to overcome the limitations of each technology briefly described above and to improve the
positioning accuracy of the navigation, the integration of multiple sensors is unavoidable. More
recently, many multi-sensor PNSs have been developed experimentally and commercially [23-27].
Regarding the PNSs, the implementation of a simulation environment is a major step forward to
design and evaluation of the algorithms and sensor integration approaches. In the literature, there are a
few studies in the field of PNS simulators [28-31], and there is still a long way to go [32].
1.2. Motivation and novelty
In ASELSAN's self-funded research project entitled "Positioning in GNSS Denied Environments
(labeled as UDOKS)", we aim to develop seamless navigation systems for pedestrians in GNSS-denied
environments. To achieve this aim, among alternative sensor technologies, inertial sensors (i.e.,
gyroscopes and accelerometers), UWB ranging sensors, and aiding sensors such as magnetometers and
barometers are taken into consideration to integrate. Furthermore, as a part of UDOKS project, a
simulation environment allowing the user to evaluate PNSs integrating not only the aforementioned
sensor technologies but also other alternatives is being implemented.
The idea in UDOKS project is to assist the inertial and aiding sensors-based PNS with UWB range
estimates not only in environments with pre-installed infrastructures (via UWB absolute positioning
solutions) but also in environments where such an infrastructure is not available (via UWB relative
positioning solutions). Additionally, to the best of the authors’ knowledge, there has not been any other
simulation environment in the literature for the aim of the design and evaluation of PNSs which is more
comprehensive than that being implemented in UDOKS project and outlined generally in this Work-in-
Progress (WiP) paper.
The remainder of the paper is organized as follows: The criteria for the selection of sensors to be
integrated and the sensor integration concept of the PNS being developed in UDOKS project are
described in Section 2. The architectural details of the simulation environment are provided in Section
3. Preliminary indoor performance results of the pure inertial sensors-based PNS developed as a single
navigation system are given in Section 4. Finally, the paper is concluded in Section 5.
2. Pedestrian navigation system being developed in UDOKS project
The next subsections describe the criteria for the choice of the sensors to be included by the PNS
being developed in UDOKS project and their integration concept.
2.1. Choice of the sensors
Inertial Navigation Systems (INSs) are well-suited for pedestrian navigation purposes in GNSS-
denied environments. An INS is composed of a computational unit and an Inertial Measurement Unit
(IMU) which typically consists of 3-axis gyroscopes to measure angular rates and 3-axis accelerometers
to measure specific forces. The main problem with INSs is that they suffer from integration drift. When
using inertial sensors for calculating orientation, velocity and position, the inertial sensor measurements
are integrated. These integrations cause any error in the sensor measurements to accumulate over time
and create drifts in navigation estimates. Due to this inherent drift problem, the performance of an
inertial sensors-based PNS relies deeply on having effective methods for drift correction, and
integrating aiding sensors and drift-free absolute positioning systems other than GNSS in GNSS-denied
environments. Nowadays, in addition to gyroscopes and accelerometers, the sensor combination in an
IMU is generally augmented with 3-axis magnetometers and a barometer which are used for aiding the
heading and altitude estimates, respectively. One way to correct the integration drift in an inertial
sensors-based PNS is strapping the IMU down to the pedestrian's foot and introducing Zero Velocity
Updates (ZUPTs) [33] to the INS mechanization during standstill periods of foot motion. Other drift
correction methods, e.g., Zero Angular Rate Update (ZARU) [34] and Heuristic Drift Reduction (HDR)
[35], are also available and they will be taken into account in our studies. Tactical and industrial grade
foot-mounted IMUs will be employed as the heart of the PNS which is being developed in UDOKS
project.
In addition to the foot-mounted IMU and aiding sensors such as magnetometers and a barometer,
Ultra Wideband (UWB) ranging sensors will be used in UDOKS project as a complementary sensor
technology for providing drift-free absolute position values. UWB, among Radio Frequency (RF)
technologies, is the most promising one due to its range estimation capability much more precise than
of others such as Wi-Fi and Bluetooth Low Energy (BLE) [36]. Large bandwidth of UWB signals brings
many advantages for positioning such as penetration through obstacles and immunity to multipath
fading [37]. Moreover, UWB technology allows simultaneous position and data transmission, and
combines remarkable features concerning size and power consumption [38]. To perform absolute
positioning with UWB sensors, a global or local reference coordinate system must be created and
absolute position of each UWB sensor must be described with respect to this reference coordinate
system. The reference coordinate system is developed by using UWB sensors referred to as anchors.
The initial position assignment of anchors can be done by placing them on positions with known
coordinates or on positions with coordinates which are estimated during navigation. Then, by using this
developed coordinate system, absolute positions of mobile UWB sensors, referred to as tags, are
calculated. For two-dimensional absolute positioning at least three anchors, and for three-dimensional
positioning at least four anchors are needed. Relative positioning, on the other hand, relies only on the
pairwise distance estimates between UWB sensors to define their positions with respect to an arbitrary
internal coordinate system. Relative positioning does not require any prior position information or an
external infrastructure such as GNSS signals, landmarks or beacons. Finding the positions of UWB
sensors relative to each other is considered as an essential need in many applications which require
autonomy and cooperation [39].
2.2. Integration concept for the chosen sensors
In UDOKS project, a foot-mounted IMU, aiding sensors and UWB ranging sensors will be
integrated to develop a PNS. A prospective concept for the sensor integration is illustrated in Figure 1.
Figure 1: A prospective concept for the sensor integration of the PNS being developed in UDOKS
project.
The basic idea of the integration concept is to use a Kalman-based filter (i.e., linear-KF, extended-
EKF or unscented-UKF) to estimate the error states related to INS mechanization. The Kalman-based
filter is updated with pseudo-measurements coming from drift correction methods and the updated error
estimates are applied into INS mechanization. The inclusion of the drift correction methods in Figure 1
will be such comprehensive that it enables the Kalman-based filter to estimate the errors of all
navigation states (i.e., ZUPT will provide velocity-, roll- and pitch-correcting measurements,
magnetometers will provide heading-correcting measurements, barometer will provide altitude-
correcting measurements, and UWB will provide position-correcting measurements).
3. Simulation environment being implemented
In parallel with the PNS development efforts described in previous section, a simulation
environment is also being implemented as a part of UDOKS project. We strive to implement a
simulation environment to evaluate and compare several pedestrian navigation algorithms by
considering different usage scenarios and error levels of integrated sensors. To achieve this goal, the
simulation environment which is being implemented should have an open and modular architecture
with the capability of inserting and integrating appropriate navigation algorithms and complementary
sensor technologies.
A prospective architecture for the simulation environment is illustrated in Figure 2. The simulation
environment generates a trajectory in navigation frame according to the usage scenario. The generated
navigation signals are then transformed into ideal sensor signals in pedestrian's body frame. Different
types of sensor errors with deterministic and stochastic models are injected into the ideal signals
concerning the qualities of the sensors to be integrated. The heterogeneity of the data coming from
different sensor technologies is being dealt with suitable integration and data fusion techniques.
Simulation environment allows the user to choose different sensor combinations which are compatible
with the usage scenario. The prospective architecture of the simulation environment naturally includes
the sensor combination of the PNS being developed in UDOKS project. Additionally, it includes the
sensor technologies such as Wi-Fi, BLE, ultrasound [40], and GNSS (even if UDOKS project aims to
develop a PNS for GNSS-denied environments, the simulation environment comprises GNSS
integration assuming that for some usage scenarios GNSS is available and trusted). Thanks to the open
and modular architecture of the simulation environment, along with the sensor technologies previously
mentioned, the designer will have an ability to integrate other alternative technologies agreeable with
the usage scenario.
Figure 2: A prospective architecture for the simulation environment being implemented in UDOKS project.
4. Preliminary performance results
Preliminary performance results of the pure inertial sensors-based PNS developed as a single
navigation system are given in this section. The developed PNS consists of only a STIM300 IMU [41]
as a sensor module attached to the foot as shown in Figure 3.
Figure 3: STIM300 attached to the foot via shoe’s laces.
The test was conducted in an indoor environment on a rectangular path 105 meters long. Walking
the path in clockwise direction took 90 seconds. All gyroscope and accelerometer data were logged and
processed afterwards using MATLAB. The conducted walking began and ended at the same point of
the path. Standstill detection was carried out by utilizing the Stance Hypothesis Optimal Estimation
(SHOE) detector [42] and high detection accuracies were generally obtained. ZUPT was used for drift
correction and the errors on the navigation parameters were estimated by an Extended Kalman Filter
(EKF).
Figure 4 shows the estimated position outputs of the PNS being developed in UDOKS project. As a
preliminary performance index, the difference between the initial (the origin) and final estimated
positions with respect to total distance walked is calculated. The distance between the starting point and
final estimated position is 0.35 meter. Thus, the positioning error is 0.33% of the distance walked.
Figure 4: Preliminary indoor performance evaluation of the pure inertial sensors-based PNS developed
in UDOKS project.
5. Conclusions
Developing accurate and robust navigation systems for pedestrians in GNSS-denied environments
is a big challenge that has been attracting huge attention from the research, development, and scientific
communities in recent years. UDOKS, ASELSAN's ongoing self-funded research project, aims at
developing a PNS integrating inertial sensors, UWB ranging sensors, and aiding sensors such as
magnetometers and a barometer. Traditional and proven navigation algorithms such as INS
mechanization and error state estimation via Kalman-based filters will be utilized by the PNS being
developed in UDOKS project. Moreover, some novel approaches, relatively newer and well-
functioning methods will also be utilized to detect the standstill periods of foot motion and to bound
the integration drifts in the navigation solution. Especially, assisting the inertial and aiding sensors-
based PNS with UWB relative positioning solutions in a cooperative navigation concept will be one of
the primary purposes of UDOKS project studies.
In addition to the PNS development efforts, a simulation environment with an open and modular
architecture is also being implemented in UDOKS project. The simulation environment is expected to
be a tool enabling the designer to test and evaluate new approaches and different sensor combinations
during earlier phases of the PNS developments in the future.
6. Acknowledgements
The authors would like to thank Ahmet Levent Ergün, Ayşe Deniz Duyul Çakmak, Ertuğrul Aksoy,
Sertaç Çakır and Talha İnce for their support throughout the work.
7. References
[1] D. Niculescu, B. Nath, Ad hoc positioning system (APS), IEEE global telecommunications
conference, pp. 2926-2931, 2001, doi: 10.1109/GLOCOM.2001.965964.
[2] L. Zheng, W. Zhou, W. Tang, X. Zheng, A. Peng, H. Zheng, A 3D indoor positioning system based
on low-cost MEMS sensors, Simulation modelling practice and theory, vol. 65, pp. 45-56, 2016,
URL: https://doi.org/10.1016/j.simpat.2016.01.003.
[3] X. Hou, J. Bergmann, Pedestrian dead reckoning with wearable sensors: a systematic review, IEEE
sensors journal, vol. 21, no. 1, pp. 143-152, 2021, doi: 10.1109/JSEN.2020.3014955.
[4] E. Foxlin, Pedestrian tracking with shoe-mounted inertial sensors, IEEE computer graphics and
applications, vol. 25, no. 6, pp. 38-46, 2005, doi: 10.1109/MCG.2005.140.
[5] S. Godha, G. Lachapelle, Foot mounted inertial system for pedestrian navigation, Measurement
science and technology, vol. 19, no. 7, 2008.
[6] C. Fischer, P. T. Sukumar, M. Hazas, Tutorial: implementing a pedestrian tracker using inertial
sensors, IEEE pervasive computing, vol. 12, no. 2, pp. 17-27, 2013, doi: 10.1109/MPRV.2012.16.
[7] D. B. Ahmed, E. M. Diaz, S. Kaiser, Performance comparison of foot- and pocket-mounted inertial
navigation systems, 2016 International conference on indoor positioning and indoor navigation
(IPIN), pp. 1-7, 2016, doi: 10.1109/IPIN.2016.7743673.
[8] Y. Hsu, J. Wang, C. Chang, A wearable inertial pedestrian navigation system with quaternion-
based extended Kalman filter for pedestrian localization, IEEE sensors journal, vol. 17, no. 10, pp.
3193-3206, 2017, doi: 10.1109/JSEN.2017.2679138.
[9] N. Yu, Y. Li, X. Ma, Y. Wu, R. Feng, Comparison of pedestrian tracking methods based on foot-
and waist-mounted inertial sensors and handheld smartphones, IEEE sensors journal, vol. 19, no.
18, pp. 8160-8173, 2019, doi: 10.1109/JSEN.2019.2919721.
[10] M. Kok, J. D. Hol, T. B. Schön, Using inertial sensors for position and orientation estimation,
Foundations and trends® in signal processing, vol. 11, no. 1-2, pp. 1-153, 2017, URL:
http://dx.doi.org/10.1561/2000000094.
[11] X. Yun, J. Calusdian, E. R. Bachmann, R. B. McGhee, Estimation of human foot motion during
normal walking using inertial and magnetic sensor measurements, IEEE transactions on
instrumentation and measurement, vol. 61, no. 7, pp. 2059-2071, 2012, doi:
10.1109/TIM.2011.2179830.
[12] T. Gädeke, J. Schmid, M. Zahnlecker, W. Stork, K. D. Müller-Glaser, Smartphone pedestrian
navigation by foot-IMU sensor fusion, 2012 Ubiquitous positioning, indoor navigation, and
location based service (UPINLBS), pp.1-8, 2012, doi: 10.1109/UPINLBS.2012.6409787.
[13] Q. L. Yuan, I. M. Chen, Simultaneous localization and capture with velocity information,
IEEE/RSJ international conference on intelligent robots and systems, 2011, doi:
10.1109/IROS.2011.6094447.
[14] C. Fischer, K. Muthukrishnan, M. Hazas, H. Gellersen, Ultrasound-aided pedestrian dead
reckoning for indoor navigation, ACM international workshop on mobile entity localization and
tracking in GPS-less environments, pp. 31-36, 2008, doi: 10.1145/1410012.1410020.
[15] D. D. Pham, Y. S. Suh, Pedestrian navigation using foot-mounted inertial sensor and LIDAR,
Sensors, vol. 16, no. 1, pp. 120-136, 2016, doi: 10.3390/s16010120.
[16] R. Mautz, S. Tilch, Survey of optical indoor positioning systems, 2011 International conference
on indoor positioning and indoor navigation (IPIN), pp. 1-7, 2011, doi:
10.1109/IPIN.2011.6071925.
[17] C. Fischer, K. Muthukrishnan, M. Hazas, Chapter 3 – SLAM for pedestrians and ultrasonic
landmarks in emergency response scenarios, Advances in computers, vol. 81, pp. 103-160, 2011,
URL: https://doi.org/10.1016/B978-0-12-385514-5.00003-3.
[18] C. Loconsole, M. B. Dehkordi, E. Sotgiu, M. Fontana, M. Bergamasco, A. Frisoli, An IMU and
RFID-based navigation system providing vibrotactile feedback for visually impaired people,
International conference on human haptic sensing and touch enabled computer applications, pp.
360-370, 2016, URL: https://doi.org/10.1007/978-3-319-42321-0_33.
[19] S. He, S.-G. Chan, Wi-fi fingerprint-based indoor positioning: recent advances and comparisons,
IEEE communications surveys & tutorials, vol. 18, no. 1, pp. 466-490, 2016, doi:
10.1109/COMST.2015.2464084.
[20] Q. Fan, B. Sun, Y. Sun, X. Zhuang, Performance enhancement of MEMS-based INS/UWB
integration for indoor navigation applications, IEEE sensors journal, vol. 17, no. 10, pp. 3116-
3130, 2017, doi: 10.1109/JSEN.2017.2689802.
[21] F. Campana, A. Pinargote, F. Domínguez, E. Peláez, Towards an indoor navigation system using
bluetooth low energy beacons, 2017 IEEE second ecuador technical chapters meeting (ETCM), pp
1–6, 2017, URL: https://doi.org/10.1109/ETCM.2017.8247464.
[22] Y. Wang, X. Ma, G. Leus, Robust time-based localization for asynchronous networks, IEEE
transactions on signal processing, vol 59, no. 9, pp. 4397-4410, 2011, doi:
10.1109/TSP.2011.2159215.
[23] J. Rantakokko, J. Rydell, P. Strömbäck, P. Händel, J. Callmer, D. Törnqvist, F. Gustafsson, M.
Jobs, M. Grudén, Accurate and reliable soldier and first responder indoor positioning: multisensory
systems and cooperative localization, IEEE wireless communications, vol. 18, no. 2, pp.10-18,
2011, doi: 10.1109/MWC.2011.5751291.
[24] A. Morrison, V. Renaudin, J. B. Bancroft, G. Lachapelle, Design and testing of a multi-sensor
pedestrian location and navigation platform, Sensors, vol. 12, no. 3, pp. 3720-3738, 2012, doi:
10.3390/s120303720.
[25] A. D. Monica, L. Ruotsalainen, F. Dovis, Multisensor navigation in urban environment, IEEE/ION
position, location and navigation symposium (PLANS), pp. 730-738, 2018, doi:
10.1109/PLANS.2018.8373448.
[26] NEON personnel tracker pro, URL: https://www.trxsystems.com/personnel-tracker.html.
[27] Warloc, URL: https://www.roboticresearch.com/warloc/.
[28] C. Ascher, C. Kessler, A. Maier, P. Crocoll, G. F. Trommer, New pedestrian trajectory simulator
to study innovative yaw angle constraints, 23rd International technical meeting of satellite division
of the institute of navigation (ION GNSS+), pp. 504-510, 2010.
[29] F. J. Zampella, A. R. Jimenez, F. Seco, J. C. Prieto, J. I. Guevara, Simulation of foot-mounted IMU
signals for the evaluation of PDR algorithms, 2011 International conference on indoor positioning
and indoor navigation (IPIN), pp. 1-7, 2011, doi: 10.1109/IPIN.2011.6071930.
[30] A. D. Young, M. J. Ling, D. K. Arvind, IMUSim: A simulation environment for inertial sensing
algorithm design and evaluation, 10th ACM/IEEE international conference on information
processing in sensor networks, pp. 199-210, 2011.
[31] A. Taylor, G. Hsu, B. Oh, What is the pedestrian dead reckoning accuracy that can be achieved
with todays MEMS sensors in mobile phones and why is it important?, 27th International technical
meeting of satellite division of the institute of navigation (ION GNSS+), pp. 125-140, 2014.
[32] D. B. Ahmed, L. E. Díez, E. M. Diaz, J. J. G. Dominguez, A survey on test and evaluation
methodologies of pedestrian localization systems, IEEE sensors journal, vol. 20, no. 1, pp. 479-
491, 2020, doi: 10.1109/JSEN.2019.2939592.
[33] J. Wahlström, I. Skog, Fifteen years of progress at zero velocity: a review, IEEE sensors journal,
vol. 21, no. 2, pp. 1139-1151, 2021, doi: 10.1109/JSEN.2020.3018880.
[34] S. Rajagopal, Personal dead reckoning system with shoe mounted inertial sensors, Master’s thesis,
Royal Institute of Technology (KTH), Stockholm, Sweden, 2008.
[35] J. Borenstein, L. Ojeda, S. Kwanmuang, Heuristic reduction of gyro drift for personnel tracking
systems, Journal of navigation, vol. 62, no. 1, pp. 41-58, 2009, doi: 10.1017/S0373463308005043
[36] M. Ghavami, L. Michael, R. Kohno, Ultra wideband signals and systems in communication
engineering, John Wiley and Sons, 2007.
[37] J. Y. Lee, Ultra-wideband ranging in dense multipath environment, Ph.D. thesis, University of
Southern California, Los Angeles, CA, 2002.
[38] L. Yang, G. B. Giannakis, Ultra-wideband communications: an idea whose time has come, IEEE
signal processing magazine, vol. 21, no. 6, pp. 26-54, 2004, doi: 10.1109/MSP.2004.1359140.
[39] S. Zobar, E. Aksoy, An application of relative node positioning using ultra-wideband distance
estimates, NATO SET-275 symposium on cooperative navigation in GNSS degraded and denied
environments, 2021.
[40] O. N. Güneş, E. Aksoy, S. Zobar, A multi-dimensional scaling application with ultra-wideband
and ultrasound ranging, 28th Signal processing and communications applications conference (SIU),
pp. 1-4, 2020, doi: 10.1109/SIU49456.2020.9302428.
[41] STIM300, URL: https://www.sensonor.com/products/inertial-measurement-units/stim300/.
[42] I. Skog, P. Handel, J. Nilsson, J. Rantakokko, Zero-velocity detection-an algorithm evaluation,
IEEE transactions on biomedical engineering, vol. 57, no. 11, pp. 2657-2666, 2010, doi:
10.1109/TBME.2010.2060723.