S2

iag-comm4-2022
SC41: Emerging Positioning Technologies and GNSS Augmentations
Convener: L. M. Ruotsalainen | Co-convener: Ruizhi Chen
Orals
| Mon, 05 Sep, 10:00–15:00 (CEST)|Wissenschaftsetage Potsdam

Orals: Mon, 05 Sep | Wissenschaftsetage Potsdam

10:00–10:30
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iag-comm4-2022-55
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invited
Naser El-Sheimy

There are three ‘pillars’ that define the performance or usefulness of a navigation technologies – cost, accuracy, and continuity. Navigation is a field that has been fascinating humankind for thousands of years and these pillars have been evolving with new technological advancements.  The current market in positioning and navigation is clearly dominated by GNSS. Besides being globally available, it meets two important pillars: accuracy and cost by providing the whole range of navigation accuracies at very low cost. It is also highly portable, has low power consumption, and is well suited for integration with other sensors, communication links, and databases.

At this point in the development of navigation technology, the need for alternative positioning systems only arises because GNSS does meet the continuity pillar as it does not work in all environments. Furthermore, there has been a constant market push to develop navigation systems that are accurate, continuous and easy to afford. Needless to say, that cost, and space constraints are currently driving manufacturers of cars, portable devices (e.g. smartphones), and autonomous systems (e.g. self-driving, drones and agriculture machine systems) systems to investigate and develop next generation of low cost and small size navigation systems to meet the fast-growing autonomous vehicles and location services market demands. This presentation will provide a state of the art and future trends of sensors used for navigation of autonomous vehicles: possibilities, limitations and various design approaches.  Emphasis will be on sensors and technologies that can navigate autonomous vehicles everywhere and at any time independent of weather and light conditions. Some of the current developed and possible future system’s accuracy performance will be demonstrated through different implementations/applications using Propound Positioning Inc technologies.

How to cite: El-Sheimy, N.: Navigation Technologies for Future Autonomous Vehicles, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-55, https://doi.org/10.5194/iag-comm4-2022-55, 2022.

10:30–10:50
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iag-comm4-2022-4
Ganesh Kumar, Sharnam Shah, Yongbo Qian, Nahid Pervez, and Tyler Reid

Computing the GNSS Sky-View Factor in Urban Landscapes for Autonomous Driving Simulation

 

GANESH P KUMAR, SHARNAM SHAH, YONGBO QIAN, NAHID PERVEZ

Ford Greenfield Labs • Palo Alto • California • 94043 USA

Email: (gkumar29, sshah89, npervez2, yqian17)@ford.com

 

TYLER G R REID

Xona Space Systems • Vancouver • British Columbia • V6R 2G6 Canada

Email: tyler@xonaspace.com

 

Keywords: GNSS, Sky-View Factor (SVF), Line of Sight (LOS), Multipath, Autonomous Vehicle (AV)

 

Motivation

It is often cost- and risk-effective to test the response of an Autonomous Vehicle (AV)’s planning and control module to simulated perception output from its sensors, an exercise called perception simulation [1]. The AV’s GNSS Sensor (called the ego GNSS) computes its position in a world coordinate frame (e.g., WGS-84) and feeds directly into the AV’s localization module, influencing downstream operations such as map-relative localization and sensor fusion.  Consequently, predicting or simulating GNSS output is extremely useful to determine roadways wherein AV localization may experience degraded performance.

 

However, GNSS output is generally challenging to simulate [4] due to the multiple time- and location-varying sources of error that impact its operation. City landscapes pose modelling challenges in that their buildings and urban canopies (referred to as topographic elements from now) limit the ego GNSS’ view of satellites in the sky, causing sky-impairment [14] that impacts GNSS availability. Further, these topographic elements also precipitate multipath and non-line of sight (NLOS) effects that impact GNSS accuracy.

 

Sky-View Factor (SVF): Since modeling (or simulating) these effects in their entirety for a given urban landscape is computationally nontrivial - primarily due to the need to capture the interference of radio-frequency waves interacting with each topographic element – we will focus on the more tractable problem of computing the Sky-View Factor (SVF) of the landscape. We define the SVF of the (ego) GNSS with respect to a landscape to be fraction of the sky visible to it, unobscured by topographic elements [13]; the SVF is thus a dimensionless quantity between zero (representing a completely obscured sky) and unity (representing a fully unobscured sky), representing the complement of sky impairment. When its SVF is unity, the GNSS’ sky visibility is blocked only by the earth’s curvature, and the GNSS receiver can view the maximum possible number of satellites in its line of sight (LOS).

 

The motivation behind the choice of the SVF as our metric of interest are:  its computation is a tractable geometric problem determined only by the shapes of the topographic elements and the GNSS receiver location; the number of satellites visible to the user in its LOS may be determined from it; the data structures used in its computation may be used as a precursor to more complex models of accuracy and availability; it distills a landscape into a single scalar metric that measures how close the AV is located to a city center (or a location rich in topographic elements) - and it may thus be used to characterize cities; and prior work does not compute it directly except for specific dispositions of topographic elements [13].

Thus, the novelty of this abstract lies in identifying and solving the problem of computing the SVF, suggesting approaches to speed up the computation (at the expense of accuracy) for real time applications and outlining further applications of SVF-related data structures. 

 

Terminology

We paraphrase the following definitions from [14]. A GNSS constellation consists of a satellite set that provides position, navigation, and timing (PNT) information to a GNSS Receiver that is usually located on the earth’s surface. Traditional GNSS constellations reside in Medium Earth Orbit, for example, GPS at an altitude of approximately 20,200 km. Historical GNSS constellation orbital data is available online for example, at [10], although this framework also allows us to examine future satellites including commercial Low Earth Orbit (LEO) Position, Navigation, and Time (PNT) satellites via simulation. We denote the altitude of satellites in a constellation of interest by Rsat. We will also use GPS to mean GNSS receiver throughout. The pseudo-range equation is used to compute the ego position on the earth’s surface using the satellites visible to the GPS. Multipath refers to the reflection of GPS signals off multiple surfaces (e.g., those of buildings) before reaching the GPS receiver, leading to degraded accuracy.

 

Prior Work

The pseudo-range GPS equation, sources of GPS error, and satellite navigation performance metrics including availability and accuracy are detailed in [14]. The significance and challenges of GPS modeling for perception simulation are noted in [4, 6], while [11, 20] specify approaches to computing multipath effects in urban environments.  Multipath and NLOS effects are computed using simulators in [15, 21]. Our prior work [17] measures the difference between automotive and RTK GPS receiver accuracy over North American Highways. The Sky-View Factor, a term largely used in building and environmental research, is defined and computed in [13]. 

 

Problem Statement

Given the following data: 1. a discrete time interval (t0, t0 + δt , t0 + 2δt, ...,tf)   sampled every δt seconds (the sampling frequency or GPS Epoch), 2. the map of an urban landscape defined by topographic elements  T={Ti: 1 ≤ i  ≤ n}, specified in WGS-84 coordinates (taken, e.g., from OpenStreetMap or Google Street View, comprising latitude, longitude and altitude), with each roadway taken to be a polygon and each topographic element taken to be a polyhedron specified by its vertices or faces; 3. the position of an AV specified in WGS-84 coordinates (on e.g., a roadway) in the map; 4. satellite position data for the given time interval (e.g., from RINEX files), for satellite set S={Si: 1 ≤ i  ≤ l}, each orbiting the earth at altitude Rsat above the earth's surface.

 

Problem: Determine the SVF of the ego GPS over the given time interval. Further, determine those satellites that are in the GPS LOS.

 

Computing SVF for a single time point: Take a single epoch t in the given time interval. At t let G=(lat, lon, alt)  be the vehicle position on the earth.  Ignoring topography, the area of the sky that is visible to the GPS receiver in the absence of topographic elements is given by the base of a cone with apex G , slant distance Rsat, and a planar apex angle θ1.  This visible area  Avis lies between 0 and 4πR2sat, where the factor of 4π corresponds to the solid angle subtended by a sphere at its center; the resulting SVF is unity (unless Avis = 0, in which case the SVF is undefined).

We will now determine the impact of a single (polyhedral) element T1 on the SVF. First, we compute the polygonal face of T1 visible G to by constructing straight line segments from G to each vertex of T1 and discarding those vertices that are occluded from by another point in T1. We call this polygon Vis(T1), and suppose that its vertices are  {Vi}. Subsequently, we construct a blocked spherical pyramid Pyr1 with apex G and edges of the form GVi extended to distance Rsat; none of the points in the interior of Pyr1  is visible to G. The base of this pyramid rests on a subset of the unobscured visible area Avis computed earlier. We may compute the area of this pyramidal base to be A1. Since this area is obscured, the resulting visible area shrinks to Avis - A1, and the SVF becomes 1-A1 /Avis.

To extend this computation to an urban landscape comprising multiple polyhedral elements, we first compute their union using (e.g.) [2] and apply the algorithm above to the resulting solid to determine the region of blockage. Note that this blocked region is in general a concave union of convex polyhedra, resting on a spherical base.

Extension of SVF to a time interval: Repeating this algorithm at every time point in the given interval is inefficient. To extend the computation from time instant to the next epoch t+δt, we drop the contributions from topographic elements visible at time t, and correspondingly add the contributions of those visible at t+δt. Elements common to consecutive time instants may be identified by pre-processing the urban landscape for the entirety of the time interval, and thus used to speed up the computation over the interval.

Speeding up SVF Computation: When each topographic element is a cuboid oriented vertically to the earth’s surface (e.g., each is a building), this computation can be simplified using a plane sweep algorithm [3]. To get a quick (if inaccurate) estimate of the SVF in real time, we may replace every polyhedron with its (cuboidal) bounding box and repeat the sweep algorithm.

Determining satellites in the LOS:   At any time instant t we may determine if satellite Si is in the LOS by determining if the line segment GSi  lies fully outside the blocked region of the GPS receiver. This may be done (e.g.) by testing whether the segment intersects any of the faces of the blocked region, using standard approaches from computational geometry [22]. We repeat this computation for every satellite to determine the subset of the satellite set that lies in the receiver's LOS.

Current Plan and Future Work

We are currently examining various cityscapes from OpenStreetMap building data [16] to give as input to our algorithm, and thereby model GNSS geometry and visibility (to a first approximation) in perception simulation. We are also currently working with external partners who specialize in creating virtual 3-D models of the earth for higher fidelity modelling. In future work, we will use the techniques in [14] to get a first approximation of the multipath and NLOS component of the measurement noise.  This is expected to be a significant extension to our current algorithm.

  

References  

    1. Applied Intuition. “From Research to Production: Strategies for Perception Simulation (Part 1 of 2)”. Available: https://blog.applied.co/blog-post/research-to-production (2021).   

    2. Aronov, Boris, Micha Sharir, and Boaz Tagansky. "The union of convex polyhedra in three dimensions." SIAM Journal on Computing 26, no. 6 (1997). 

    3. De Berg, Mark, et al. "Computational geometry: introduction." Computational geometry: algorithms and applications (2008). 

    4. Durst, Phillip Jefferson, and Christopher Goodin. "High fidelity modelling and simulation of inertial sensors commonly used by autonomous mobile robots." World J. Model. Simul 8.3 (2012): 172-184.   

    5. Elango, G. Arul, and G. F. Sudha. "Design of complete software GPS signal simulator with low complexity and precise multipath channel model." Journal of electrical systems and information technology 3.2 (2016): 161-180. 

    6. Elmquist, Asher, and Dan Negrut. "Methods and models for simulating autonomous vehicle sensors." IEEE Transactions on Intelligent Vehicles 5.4 (2020): 684-692.  

    7. Gowdayyanadoddi, Naveen S., et al. "A ray-tracing technique to characterize GPS multipath in the frequency domain." (2015).   

    8. von Hünerbein, Karen, and Grégory Moura. "A new tool for simulation of geospecific multipath and obscuration of GPS/GNSS signals with relation to realistic 3-D city models." 

    9. Hussain, Arif, et al. "Adaptive GNSS Receiver Design for Highly Dynamic Multipath Environments." IEEE Access 8 (2020): 172481-172497. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.927.3102&rep=rep1&type=pdf  (2022). 

    International GNSS Service (IGS), “Downloadable Constellation Data”. Available: https://www.igs.org/network#downloadable (2022). 

   10. Lau, Lawrence, and Paul Cross. "Development and testing of a new ray-tracing approach to GNSS carrier-phase multipath modelling." Journal of Geodesy 81.11 (2007): 713-732.   

   11. Li, ChengJun, et al. "The design and implement of GPS software simulation platform." 2008 Asia Simulation Conference-7th International Conference on System Simulation and Scientific Computing. IEEE, 2008. 

   12. Miao, Chunping, et al. "Review of methods used to estimate the sky view factor in urban street canyons." Building and Environment 168 (2020): 106497. 

   13. Morton, Y. Jade, et al., eds. Position, navigation, and timing technologies in the 21st century: Integrated satellite navigation, sensor systems, and civil applications, volume 1 and 2. John Wiley & Sons, 2021.   

   14. Nievinski, Felipe G., and Kristine M. Larson. "An open source GPS multipath simulator in Matlab/Octave." Gps Solutions 18.3 (2014): 473-481. 

   15. OpenStreet MapBuilding Data. Available: https://taginfo.openstreetmap.org/keys/building (2022). 

   16. Reid, Tyler GR, et al. "Standalone and RTK GNSS on 30,000 km of North American Highways." Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019). 2019.  

   17. Spirent. “GNSS, Multipath Simulation in 3D - Spirent.” Multipath and Obscuration Simulation, www.spirent.com/products/multipath-and-obscuration-simulation-sim3d (2022). 

   18. StackExchange Inc. “How much of the sky is visible from a particular location?”. Available: https://physics.stackexchange.com/questions/129317/how-much-of-the-sky-is-visible-from-a-particular-location (2022). 

   19. Suzuki, Taro, and Nobuaki Kubo. "Correcting GNSS multipath errors using a 3D surface model and particle filter." Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013). 2013.   

   20. Suzuki, Taro, and Nobuaki Kubo. "Simulation of GNSS satellite availability in urban environments using Google Earth." Proceedings of the ION 2015 Pacific PNT Meeting. 2015.  

   21. Tananyagtár ,Gyires Béla. “Point Containment Algorithms in Computer Graphics”. Available: https://gyires.inf.unideb.hu/KMITT/a52/ch10s04.html#:~:text=Polyhedron%2Dpoint%20containment%20test.,number%20of%20intersections%20is%20counted (2022). 

   22. US Department of Defense, “GPS Standard Positioning System Performance Standard”. Available: https://www.gps.gov/technical/ps/2020-SPS-performance-standard.pdf#page=67 (2020)   

How to cite: Kumar, G., Shah, S., Qian, Y., Pervez, N., and Reid, T.: Computing the GPS Sky-View Factor in Urban Landscapes for Autonomous Driving Simulation, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-4, https://doi.org/10.5194/iag-comm4-2022-4, 2022.

Coffee break
11:20–11:40
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iag-comm4-2022-5
Jelena Gabela, Günther Retscher, Andrea Masiero, and Charles Toth

The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.

How to cite: Gabela, J., Retscher, G., Masiero, A., and Toth, C.: Seamless Indoor-Outdoor Transitioning of Pedestrian Platforms, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-5, https://doi.org/10.5194/iag-comm4-2022-5, 2022.

11:40–12:00
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iag-comm4-2022-32
Andrew Greentree, Xuezhi Wang, Wenchao Li, brant gibson, William Moran, Liam Hall, David Simpson, and Allison Kealy

Satellite-based navigation has been a transformational technology that underpins almost all aspects of modern life. However, there are environments where global navigation satellite systems (GNSS) are not available, for example undersea or underground, and navigation that is robust to GNSS outages are also required for resilient systems.  In this context, we are exploring the potential for quantum diamond magnetometers to be used as aids for inertial measurement units for navigation in GNSS-denied environments.  We perform simulations of the magnetic field measurements combined with probabilistic data association for data mapping; and probabilistic multiple hypotheses (or Viterbi) map matching filters.  These methods are used to explore the expected navigation errors available to scalar and vector magnetometry with diamond at the sensitivities available using current and expected near-term devices.  Here we show some of our preliminary results as well as providing a broader overview of diamond as a quantum magnetometery.

How to cite: Greentree, A., Wang, X., Li, W., gibson, B., Moran, W., Hall, L., Simpson, D., and Kealy, A.: Quantum diamond magnetometry for navigation in GNSS denied environments, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-32, https://doi.org/10.5194/iag-comm4-2022-32, 2022.

12:00–12:20
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iag-comm4-2022-56
Rozhin Moftizadeh, Wenchao Li, Hamza Alkhatib, and Allison Kealy

Nowadays, in engineering geodesy, Multi-Sensor-Systems (MSSs) have gained a significant amount of interest in data acquisition.  To make sense of the derived data and use it for multiple purposes, such systems need to be localized with respect to a global coordinate system. To do so, the most straightforward way is to use the Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, such data are usually prone to errors, which should be overcome in the best way possible. One way to do so is to use beneficial information of the surrounding environment, which could be derived by other sensor types rather than the GNSS and IMU. An example of such sensors is a 3D scanner that could be used to capture the static information of a scene such as infrastructures. Moreover, the Ultra-Wide-Band (UWB) units could be used to establish a connection with the other nodes in the same environment and thus help to use potential dynamic information. Fusing various data derived from multiple sensors in a suitable filtering framework is another key to reach a reliable positioning solution. In this work, on the one hand we have explained our recent measurement campaign that was designed to cover the aforementioned aspects for capturing static and dynamic information. On the other hand, we have shown our proposed particle filtering methodology that could lead to reliable positioning solutions for MSSs that move in an inner-city area.

How to cite: Moftizadeh, R., Li, W., Alkhatib, H., and Kealy, A.: Information-Based and Cooperative Positioning of Multi-Sensor-Systems by Extended Kalman Particle Filter, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-56, https://doi.org/10.5194/iag-comm4-2022-56, 2022.

12:20–12:40
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iag-comm4-2022-49
Amarildo Haxhi, Harris Perakis, Thanassis Mpimis, and Vassilis Gikas

The release of Android Raw GNSS Measurements API in mid-2016 has enabled the reconstruction of pseudorange, carrier-phase, Doppler and carrier-to-noise density (CN0) observables in low-cost, smart devices.  This technical development has opened the door to numerous applications that necessitate increased Position-Velocity-Time (PVT) quality metrics.  However, despite the recent advances in the field, deficiencies inherent to smartphones GNSS antenna and chipset, still account for excessive biases in the raw GNSS observables and for severe multipath effects in the satellite signal.  RAIM (Receiver Autonomous Integrity Monitoring) techniques is one way to monitor and increase the performance of the PVT solution of a GNSS receiver.  It is an augmentation method to detect and eventually reject anomalous (blunders or outliers) measurements and compute protection levels that bound position errors to preset limits.

The implementation of RAIM technique to smartphone GNSS data is very limited and confined only to static positioning scenarios.  This study presents the results obtained from the testing of the classic RAIM technique in a series of kinematic positioning trials in urban environment using two cotemporary smartphone devices (i.e., Xiaomi Mi 8 and One Plus Nord2 5G).  In order to reconstruct a real use case, the smartphones were mounted on the vehicle dashboard.  The reference trajectory was obtained using a tactical grade GNSS/IMU system (NovAtel® PwrPak7, iMAR IMU-FSAS) fixed on the NTUA test vehicle roof-top sensor platform.  

At a pre-processing stage the raw GNSS data have undergone through a Hatch filer.  This step allows to smooth iteratively the noisy code pseudorange measurements using the precise (but ambiguous) phase carrier measurements.  Data analysis includes two processing strategies. We employ the PANG-NAV open-source software released by LabPANG (PArthenope Navigation Group Laboratory) to compute the SPP (Single Point Positioning) solution in two ways.  We produce the standard and the RAIM-based SPP solution using different parameter setups.  The paper presents quantitative results and comparisons between the two smartphone-based solutions and the reference trajectory, and reveal the potential (benefits and limitations) of using the RAIM technique for smartphone positioning.

How to cite: Haxhi, A., Perakis, H., Mpimis, T., and Gikas, V.: Testing of a Combined Hatch Filter / RAIM Algorithm for SPP Smartphone Kinematic Positioning in GNSS Harsh Environments, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-49, https://doi.org/10.5194/iag-comm4-2022-49, 2022.

Lunch break
13:40–14:00
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iag-comm4-2022-6
Qianwen Lin and Steffen Schön

Interference and jamming of Global Navigation Satellite System (GNSS) signals can lead to inaccurate Position, Velocity and Time (PVT) information which will result in crucial integrity and even security problems. The poor stability and accuracy of the GNSS receivers’ internal clocks, i.e. quartz oscillators, additionally impact the situation since the receiver is not able to detect the spoofing signals due to the low-quality oscillator. High-precision atomic clocks have been utilized to enhance GNSS PVT results. However, its large size, heavy weight and high-power consumption limit its deployment scenarios. Miniature atomic clock (MAC) is a promising alternative that trades off between the frequency stability and the limitations of an atomic clock.

This paper investigates the potential of chip-scale atomic clocks (CSACs) as external clocks of GNSS receivers for fingerprinting the receivers. Fingerprinting is referred to unique receiver clock features and it is characterized by receiver clock’s physical behavior like Allan Deviation (ADEV), Time Interval Error (TIE) and correlation between time series. Thus, derivation from this clock behavior can be used as an indicator for abnormal signal reception, e.g. by spoofing or jamming [1]. We gathered GNSS data observed in various scenarios. A kinematic car experiment was executed on a cart road of the south of Hannover surrounded by farmland six times [2]. About two-hours measurement data was received in 1Hz sampling rate. Another fast-driving experiment was conducted along the route consisting of a highway, an urban area in city Siegen, three tunnels and a small road with plaster, and an about 1.5 hours dataset was collected in 10Hz sampling rate. Then, a flight experiment was realized in Dortmund, with the same equipment setup and receiving about 2.5 hours data in 10Hz sampling rate [3]. For each kinematic experiment, a reference trajectory was obtained from high-quality Inertial Measurement Unit (IMU) and GNSS carrier phase measurements. Correspondingly, the operation setup of the same clocks was tested in a static condition. Each GNSS receiver of the same type (Javad TRE_G3T DELTA) either uses its internal clock, or is connected to one of the five CSACs or an atomic clock. The five CSACs, in chronological order of production, are Microsemi SA.45s CSAC, Jackson Labs CSAC, Jackson Labs OCXO, Spectratime LCR900, Microsemi MAC SA.35m. Besides, the high-precision atomic clock Standard Research Systems (SRS) PRS10 and the high-stability ovenized quartz oscillator SRS SC10 are treated as the reference. The combinations of the features, derived from the above three metrics which relate to the frequency stability of the clocks, are explored. We will show the feasibility of receiver fingerprinting with CSACs in different dynamic environments, and investigate the number of necessary features and the shortest data period to fingerprint the receivers.

 

[1] Borio, Daniele, et al. "Gnss receiver fingerprinting for security-enhanced applications." Proceedings of the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2016). 2016.

[2] Krawinkel, Thomas. “Improved GNSS navigation with chip-scale atomic clocks.” München: Verlag der Bayerischen Akademie der Wissenschaften, 2018. 2018.

[3] Jain, Ankit, and Steffen Schön. "Influence of Receiver Clock Modeling in GNSS-based Flight Navigation: Concepts and Experimental Results." 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2020.

How to cite: Lin, Q. and Schön, S.: Feasibility of CSAC-Assisted GNSS Receiver Fingerprinting in Dynamic Environments , 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-6, https://doi.org/10.5194/iag-comm4-2022-6, 2022.

14:00–14:20
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iag-comm4-2022-20
Nhung Le, Benjamin Männel, Zhiguo Deng, Thanh Thach Luong, and Harald Schuh

Abstract:

 The increasing development of GNSS techniques enables solving geodetic problems on both local and global scales. Parallelly, complex algorithms have been proposed and can also be solved well by Machine Learning (ML). However, ML techniques are sometimes not sensitive enough to gain results with a high probability for some cases, like sparse data or non-stationary GNSS time series. In this study, we use a combination of Human and Machine learning (H&M) to improve the classification performance of continuous GNSS stations. First, 427 permanent GNSS stations are obtained from the EUREF network to train ML models. The models are then applied to classify the quality of 939 continuous observation stations from two projects, EIFEL and IPOC, carried out by the German Research Centre for Geosciences (GFZ), Potsdam, Germany. Next, we independently validate the ML models' reality through a MATLAB program, GNSS metadata, and seismic data. Finally, all data of these 1366 stations are used to re-train the ML models. The main criteria to classify are the number of outliers, jumps in GNSS time series, root mean square errors, observation time-spans, and stability of the crustal motion velocity fields. Applying the approach of the H&M combination improves the performance of the ML models up to 92% while using only ML methods remains ~68%. These ML-based classification models can be applied to estimate the quality of permanent GNSS stations and to manage big databases. The result is the basis for selecting suitable control and monitoring stations in crustal deformation monitoring as well as in civil and industrial applications.

Keywords:

GNSS station classification, Machine learning, Human & Machine learning combination.

How to cite: Le, N., Männel, B., Deng, Z., Luong, T. T., and Schuh, H.: Improving Classification Performance of Continuous GNSS Stations Using a Combination of Human and Machine Learning, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-20, https://doi.org/10.5194/iag-comm4-2022-20, 2022.

14:20–14:40
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iag-comm4-2022-25
Tobias Kersten, Karol Dawidowicz, Grzegorz Krzan, Johannes Kröger, and Steffen Schön

The bottleneck of precise GNSS based applications is caused by the receiver antenna and their precision and accuracy. This work aims to contribute to the design of a model and the development of metrics to estimate and evaluate GPS/GNSS antenna calibration values in several networks. At present, there is neither information on the absolute accuracy of antenna calibration values nor a consistent handling of their uncertainties. Rather, these uncertainties repeatedly introduce inaccuracies into time series, which are used to determine global and regional reference frames as a basis for a broad variety of geophysical approaches such as meteorology, hydrography, etc. Several contributions have addressed the issue of the desired quality of GNSS receiver antenna calibrations and the assessment of their impact on GNSS data processing. All approaches lead to the open question regarding the precision and accuracy as well as stability of receiver antenna phase centre corrections. However, comprehensive and fundamental answers to this scientific question are difficult to achieve as complex interactions prevent simple estimation.

In this paper, the authors present results regarding the stability of receiver antenna calibrations in the context of time, calibration facility and strategy to help GNSS operators of regional and global GNSS stations to estimate the impact of calibration values on their sites. The quality of the calibration values also has a direct impact on the derived results, such as the zenith path delay (ZPD). We show that deviations of up to 5 mm for different high-grade antenna types occur. In addition, the normal distribution of pattern differences deviations and systematic effects have been found that lead to drifts and offsets in the parameter domain. The frequency dependence is underlined as well, e.g. L1 versus L2(P) and L2(C). The temporal stability of the calibration values is a fundamental issue, which is discussed in this context. Based on a sample of more than 30 different geodetic grade antennas, we identified variations like drifts and azimuth dependencies with magnitudes of up to 5 mm on each individual frequencies. Furthermore, the stability of the type mean versus individual calibrations shows larger differences on the signals L2 and hence leads to higher variances in the derived ionosphere-free linear combination. The results are examined using PPP time series. The derivation of metrics will be an important step to improve the consistency of regional and global GNSS products.

How to cite: Kersten, T., Dawidowicz, K., Krzan, G., Kröger, J., and Schön, S.: On the design of robust and consistent metrics for the stability of receiver antenna calibration sets, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-25, https://doi.org/10.5194/iag-comm4-2022-25, 2022.

14:40–15:00
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iag-comm4-2022-41
Harris Perakis and Vassilis Gikas

The recent advances in Information Technology (IT) and Micro-Electromechanical Systems (MEMS) technologies and their global adoption in contemporary smartphone and PDA devices enables the introduction of novel approaches for indoor positioning applications that combine multi-sensor capabilities.

In this regard, Radio-frequency (RF), high accuracy ranging technologies operating in the Ultra-Wide Band (UWB) spectrum have recently been introduced in contemporary smartphone devices. Additionally, the adoption of Wi-Fi RTT technology in the most recent smartphone devices facilitates Peer-to-Infrastructure (P2I) medium level accuracy ranging in a widespread and seamless manner together with the standard web access functionality. Applications requiring absolute positioning of moderate to low accuracy may depend on Wi-Fi RTT-only systems, while high-accuracy UWB ranging would facilitate cases demanding safety-critical proximity reliant standards. Positioning architectures optimally utilizing these Two-Way Ranging (TWR) technologies may provide increased coverage and flexibility for indoor conditions in cases that a higher level of accuracy and availability is required.

This study aims at utilizing RF-based range measurements and to combine them optimally into a Pedestrian to Infrastructure (P2I), as well as, Pedestrian to Pedestrian (P2P) collaborative functional model aided by inertial measurements for indoor positioning. The implementation of the proposed approach utilizes simulation-based TWR data from four infrastructure nodes to four roving nodes, whereas operational elements for each technology, such as sampling rate, data formatting and communications scheduling are taken into account.

The distinct working scenarios examined in this study are as follows.  Firstly, we examine separately the performance of the two technological approaches (i.e., Wi-Fi RTT and UWB) for P2I positioning scenarios.  Secondly, we study the performance of the fuse the combined use of these technologies.  For this purpose, we employ a scenario featuring a mixture of Wi-Fi RTT anchor and UWB static rover ranges. Finally, the potential (benefits and limitations) of the inclusion of IMU-based Azimuth information in the KF computation is evaluated for all scenarios. The results obtained from this investigation indicate a potential improvement in position accuracy of the order of 29% and 37% for Wi-Fi RTT/ UWB and Wi-Fi RTT/ UWB/ IMU solutions accordingly.

How to cite: Perakis, H. and Gikas, V.: Towards collaborative multi-agent positioning based on combined Wi-Fi RTT/ UWB/ IMU measurements, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-41, https://doi.org/10.5194/iag-comm4-2022-41, 2022.

Posters | Poster area

P1
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iag-comm4-2022-21
Yannick Breva and Steffen Schön

The role of codephase center corrections (CPC), also known as group delay variations (GDV), become more important nowadays, e.g. in navigation applications or ambiguity resolution. The CPC are antenna dependent delays of the received codephase, which are varying with azimuth and elevation of the incoming GNSS signal. These corrections can be estimated with a robot in the field with a similar approach as used for phase center corrections (PCC) for carrierphase measurements.
The Institut für Erdmessung (IfE) has been established an absolute calibration approach to estimate CPC and PCC for multi GNSS signals. The antenna under test (AUT) is precisely tilted and rotated around a fixed point in space by using a robot. With a reference station nearby and an external frequency standard a short-baseline common-clock setup can be achieved, which allows to calculate time-differenced single differences (dSD). The dSD can be used to estimate absolute CPC and PCC with spherical harmonics of degree and order 8. Due to highly dynamic stress, caused by the fast robot motion, a perfect tracking of the GNSS signals is challenging. This can lead to a worse repeatability of the estimated antenna pattern.
In this contribution, we are analysing different receiver settings, e.g. tracking loops, during a calibration process and their impact on the input observations (dSD) for the antenna pattern estimation, as well as their impact on the estimated CPC. Therefore, the Sx3 GNSS software receiver from the IFEN company is used, which allows to change settings in post processing using the same digitalized data stream. We show, that an optimal receiver setting can increase the repeatability of the CPC estimation.

How to cite: Breva, Y. and Schön, S.: On the impact of GNSS receiver settings on the estimation of codephase center corrections, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-21, https://doi.org/10.5194/iag-comm4-2022-21, 2022.

P2
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iag-comm4-2022-38
Ernest Ofosu Addo and Stefano Caizzone

Satellite navigation has been an important driving force of many modern technological advancements. From surveying to positioning applications, the Global Navigation Satellite System (GNSS) remains a key enabler. The obtainable quality of GNSS measurements is defined by the positioning error budget. While strong improvements can be achieved for instance, in the satellite or propagation error estimation (through use of dual frequency measurements or through use of augmentation systems), the errors generated at or close to the antenna are more difficult to estimate and counteract. Being the first element in the GNSS receiving chain, antennas significantly influence the quality of received signals.

While considerable work has been done in the last years to precisely estimate carrier phase related antenna errors, also called phase center variation (PCV) errors; the effect of the antenna on the code measurement (i.e. the analysis of code phase variations- CPV) has received less attention.However, CPV effects becomes very relevant in specific applications, like avionics or time/frequency transfer. Precise CPV determination and subsequent compensation of its related errors could be guaranteed through accurate antenna calibration. As such, good calibration methods should be further investigated and possibly standardized.

Currently, CPV estimation, similarly to PCV, can performed either in an anechoic chamber or through field observations using a robot. However, it has been observed that CPV estimated using each of these approaches tend to show mismatches. These discrepancies are thought to be related to sources such as high multipath-induced code noise and impact of receiver settings in the robot calibration method. Consequently, characterization of CPV remains a challenging task largely due to a general incomplete understanding of the complex multipath-antenna- receiver interactions. In terms of accuracy and repeatability, the levels needed in a standardized calibration procedure of CPV has not yet been met.

In this work, as a first step towards higher precision and repeatable CPV calibration, focus is placed on undertaking deeper investigation into multipath-antenna interactions and examining the contribution of multipath-induced error to the overall code-noise budget. To do this, the multipath conditions in a robot calibration site is studied using a multipath probe. This probe is an in-house developed antenna with reconfigurable electromagnetic properties and has the ability to mimic the responses of different GNSS antennas to multipath. These responses are quantified using the so-called multipath suppression capabilities indicators (MPSI) discussed by authors in previous works. This allows for substitutive characterization which is based on a proven premise that different antennas having similar MPSI (for a given spatial direction) exhibit comparable multipath suppression, i.e. receive the same amount of multipath when placed in the same scenario. Furthermore, we shall discuss the potential of using a hybrid (digital twin) simulative approach for multipath characterization. Here, anechoic chamber measurements of a given antenna are integrated in electromagnetic simulations of its anticipated surrounding physical environment. With this technique, it is possible to accurately predict and analyze sources of multipath conditions that such antenna will experience in situ. A fusion of the multipath probe’s MPSI reconfigurability and the hybrid simulative method presents a flexible but powerful tool to better understand, from the antenna viewpoint, the amount of multipath conditions on robot calibration measurements. We shall demonstrate the efficacy of this characterization technique by comparing simulated installed performance with experimental field measurements.

How to cite: Addo, E. O. and Caizzone, S.: Digital Twin Analysis for Multipath Characterization of Robot-Based GNSS Code Phase Variation Calibration Sites, 2nd Symposium of IAG Commission 4 “Positioning and Applications”, Potsdam, Germany, 5–8 Sep 2022, iag-comm4-2022-38, https://doi.org/10.5194/iag-comm4-2022-38, 2022.