=Paper=
{{Paper
|id=Vol-3719/121
|storemode=property
|title=ILEO-PNT Performance Metrics: An Extensive Comparison Between Different Constellations
|pdfUrl=https://ceur-ws.org/Vol-3719/paper3.pdf
|volume=Vol-3719
|authors=Kaan Γelikbilek,Elena Simona Lohan
|dblpUrl=https://dblp.org/rec/conf/wiphal/CelikbilekL24
}}
==ILEO-PNT Performance Metrics: An Extensive Comparison Between Different Constellations
==
https://ceur-ws.org/Vol-3719/paper3.pdf
LEO-PNT Performance Metrics: An Extensive Comparison
Between Different Constellations
Kaan Γelikbilek1,* , Elena Simona Lohan1
1
Tampere University, Korkeakoulunkatu 7, 33720, Tampere, Finland
Abstract
Recent years have shown that Low-Earth Orbit (LEO) satellites are becoming the leading idea for the future
of the space industry, gathering heavy investment from technology giants, as seen from several providers of
mega-constellations, such as SpaceX (Starlink), Eutelsat (OneWeb), Iridium, or Amazon (Kuiper). LEO satellites are
suitable not only for communication purposes, but they also hold a strong potential for Positioning, Navigation
and Timing (PNT) applications, as their proximity to Earth results in fast satellite movements in orbit as well as
in high received signal strengths, which may translate into better PNT signals compared to already available
alternatives. In this work, we show the viability of LEO-PNT constellations by providing a comprehensive
performance comparison based on coverage, Dilution of Precision (DOP) and Carrier-to-Noise Ratio (πΆ/π0 )
metrics between several existing and upcoming constellations, as well as two theoretical LEO-PNT constellations,
by considering them as dedicated LEO-PNT systems. Our results show that, among the existing and upcoming
LEO constellations, Starlink, OneWeb, Xona and Centispace show great promise for future PNT solutions, and
that alternative designs that are on par, or perhaps even better, are still possible.
Keywords
LEO Constellations, GNSS Positioning, LEO-PNT, Constellation Comparison, Link Budget Simulation
1. Introduction
1.1. Motivation for LEO-PNT Systems
Technological advances in satellite technologies have opened up the possibility of cheaper and bulk
satellite production, i.e., in the form of CubeSats [1, 2, 3, 4], which makes the concept of Low-Earth
Orbit (LEO)-based applications a highly promising and exciting new area of business and research. From
academia to industry, many players are interested in designing, launching, or innovating the existing
and upcoming LEO satellites, with promises to transform our everyday habits. Possible applications
of LEO systems are to offer broadband connectivity globally, to enable leading scientific research in
fields within the space industry, to offer Earth-sensing solutions, and, more recently, to complement
the existing Global Navigation Satellite Systems (GNSS) for robust and seamless navigation. There
are already thousands of satellites launched in LEO orbits placed between 200 and 2000 km above the
Earth [5, 6] and these satellites support various communication [7, 8, 9, 10], remote sensing [11] and
Earth sensing applications [12, 13]. It is expected that the number of LEO satellites will continue to
increase at a fast pace in the near future due to the fast time-to-market and lower launching costs
compared to Medium Earth Orbit (MEO) and Geosynchronous Equatorial Orbit (GEO) satellite launches.
The suitability of LEO satellites as Positioning, Navigation and Timing (PNT) solutions however, has not
yet been addressed extensively in the current literature and it has started to gain momentum only in the
past few years. Currently, global positioning solutions rely heavily on GNSS, which are susceptible to
malicious interferences. Therefore, there is a timely need of complementary, or even alternative, global,
and preferably low-cost, positioning methods. The Low-Earth Orbit-based Positioning, Navigation, and
Timing (LEO-PNT) concept is the perfect candidate due to LEO satelliteβs proximity to Earth, which
translates to stronger signals compared to MEO and GEO signals at similar carrier frequencies, their
WIPHAL 2024: Work-in-Progress in Hardware and Software for Location Computation, June 25-27, 2024, Antwerp, Belgium
*
Corresponding author.
$ kaan.celikbilek@tuni.fi (K. Γelikbilek); elena-simona.lohan@tuni.fi (E. S. Lohan)
0000-0001-5170-8656 (K. Γelikbilek); 0000-0003-1718-6924 (E. S. Lohan)
Β© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
capability for global coverage, and their potential for flexible, scenario-specific designs, e.g. through
optimization methods [14]. These new LEO signals could be exploited for PNT in the inevitable event
that GNSS signals become either unavailable (e.g., in deep urban canyons, under dense foliage, during
long periods of interferences) or untrustworthy (e.g., under malicious spoofing attacks).
1.2. Current LEO Landscape
The current LEO landscape includes new constellations such as Centispace [15, 16] and Xona [17],
as well as older constellations such as Orbcomm [18], Iridium [19], Globalstar [20], Starlink [8], or
Kuiper [10]. These constellations have potential for PNT services, either as signals of opportunity (e.g.,
Iridium, Starlink) or as dedicated PNT systems (e.g., Centispace, Xona). Although many aspects of these
constellations are not public knowledge, either due to legal concerns or due to design uncertainties,
research is being conducted for the different parts of LEO satellite systems in a speedy manner. For
LEO-PNT applications, research work have mostly been focused on integrated LEO and GNSS solutions
[21, 22], as well as on meta-signals and opportunistic signal frameworks [23, 24] and on alternative
positioning based on Doppler integration [25, 26]. In [27], the authors combine these three focuses
and argue that the unknown nature of the LEO satellite signals βdue to private operators tendency
to not share technical informationβ make the opportunistic approach a necessity and present their
signal model and estimation procedure for multiple scenarios involving multiple-constellation PNT
using OneWeb, Iridium NEXT, Starlink, and Orbcomm constellations. Their results show the feasibility
of LEO and GNSS integration, as well as Doppler-based positioning implementations together with
pseudorange based methods. In addition to PNT solutions, research work on LEO satellites expand
to other fields as well, such as generic constellation designs for multi-purpose applications [14, 28],
LEO-based applications for autonomous vehicles [29, 30], and LEO network design [31, 32]. The variety
of research in possible applications of LEO satellites further shows the potential benefits of LEO-PNT
solutions in the relatively near future.
1.3. Paper Goal and Contributions
Motivated by the fact that very few performance comparisons among LEO-PNT constellations have been
published so far, we present a simulation-based extensive comparison of several LEO-PNT performance
metrics for eight LEO satellite constellations (one as the GNSS benchmark, three selected among
existing mega-constellations, two selected among on-going LEO-PNT designs and two experimental
ones, based on prior work by the Authors). The comparisons are made with a MATLAB-based [33]
in-house developed constellation simulator (see section 2.3), under several indoor/outdoor scenarios.
Our main contributions are:
β’ Providing an extensive performance comparison, based on coverage, Dilution of Precision (DOP),
and Carrier-to-Noise Ratio (πΆ/π0 ) metrics between nine constellations, based on three types
of models: models relying on existing mega-constellations (Kuiper, OneWeb, Starlink), models
relying on existing smaller-sized LEO-PNT constellations (Centispace and Xona) and Authorsβ
derived models, based on direct parameter optimization [14, 4]. The European GNSS constellation
Galileo is included as a benchmark.
β’ Discussing the meaning of the obtained results under indoor and outdoor scenarios and empha-
sizing the open challenges in designing a LEO-PNT system.
β’ Providing system recommendations on constellation design aspects of possible future LEO-PNT
constellations.
Table 1
Parameters of Included Satellite Constellations (as of April 2024)
Constellation # of Satellites Altitude [km] Inclination [deg] EIRP Bandwidth Carrier Frequency
* Kuiper[10] 7774 590-650 33-80 76 dBm 400 MHz 18 GHz
* Starlink Gen-1[7] 4408 540-570 53-97.6 69.1 dBm 250 MHz 12 GHz
* Starlink Gen-2[8] 29988 340-615 33-148 69.1 dBm 250 MHz 12 GHz
* OneWeb[9] 7808 1200 40-88 65 dBm 250 MHz 12 GHz
*CentiSpace[34] 190 975-1100 55-88 ** 65 dBm ** 4.092 MHz ** 1561.098 MHz
* Xona[35] 492 ** 1080 53-90 ** 65 dBm ** 4.092 MHz ** 1561.098 MHz
Experimental 1 [14] 280 825 72 69.1 dBm 10 MHz 12 GHz
Experimental 2 [4] 420 600 76 69.1 dBm 10 MHz 12 GHz
Galileo[36] 27 23222 56 59 dBm 24.552 MHz 1575.42 MHz
* : These constellations have multiple-shells, and the indicated altitude and inclination ranges mean that individual shells have particular
values for that parameter which falls between the given ranges.
** : These parameters are based on our assumptions as exact values could not be found in public resources.
2. Methodology and Target Performance Metrics
2.1. Relevant Constellations
As mentioned in subsection 1.3, our study uses the Galileo constellation as the GNSS benchmark, and
includes 7 known LEO constellations: 3 mega constellations, 2 smaller-scale constellations, and 2
experimental ones. Even if possibly outdated, the constellation parameters (i.e., orbital altitude, number
of satellites, number of orbital planes, phasing angle between orbital planes, inclination angle of the
orbital plane, constellation topology, Right Ascension of the Ascending Node (RAAN) and eccentricity)
for the existing constellations can be obtained from public sources, with some exceptions. Most of the
relevant parameters for Xona were published in their patent application [35], however a few parameters,
i.e. the altitude and satelliteβs exact operating Effective Isotropic Radiated Power (EIRP), are not given
exactly. In a similar manner, to the best of the Authorsβ knowledge, the exact channel parameters for
Centispace are also not available publicly. Therefore, some assumptions were made as seen in Table 1
in order to fill the gaps. Since Centispace has been designed as an augmentation system for Beidou,
we assume that the missing parameters are similar to Beidouβs B1 band. As for Xona, the orbital plane
altitude is taken as the mentioned upper limit from [35], and the channel parameters are again assumed
to be similar to Beidouβs for comparison purposes with Centispaceβs constellation design.
In addition to the mentioned known constellations, we provide 2 experimental single-shell LEO-PNT
constellation designs, that have been obtained in our earlier studies [4, 14] that we name; i) "Experimental
1", and ii) "Experimental 2". The important parameters for all the relevant constellations that we selected
for our comparisons are seen in Table 1. The Starlink constellation includes two generations: Gen-1
refers to the satellites launched according to the initial constellation design from 2018 and Gen-2 refers
to the satellites launched during 2020-2022 with an alternate design.
2.2. LEO-PNT Metrics
In order to evaluate the performance of LEO-PNT constellations we selected metrics that are related to
the geometry between the users and the satellites, as well as to the reception quality of the received signal.
The metrics that we selected for our comparisons are: coverage, Geometric Dilution of Precision (GDOP),
Position (3D) Dilution of Precision (PDOP) and πΆ/π0 .
The coverage reflects the signal-reception percentage; for most PNT solutions, a good reception
requires having at least 4 satellites in view, i.e., 4-fold coverage. However, not every constellation we
use in our comparison is PNT focused, and may not be optimized for 4-fold coverage. Thus, we compute
both 4-fold and 1-fold coverage: the coverage here is computed as the percentage of the number of
users that have at least 4 and 1 satellites in view, respectively. The GDOP and PDOP are DOP metrics
that reflect the geometry of the user and the constellation [37]; the PDOP reflects the 3D position
accuracy and the GDOP reflects the joint 3D position-and-timing accuracy. Similarly, the πΆ/π0 reflects
the quality of the received signal, and it is calculated via eq. (1), where π΅π€ is the bandwidth of the
channel, and Signal-to-Noise Ratio (SNR) is calculated for each satellite-user pair.
πΆ/π0 ππ΅βπ»π§ = ππ π
+ 10πππ10 (π΅π€ ) (1)
A high PNT performance with respect to these metrics is reached for high 4-fold coverage, low values
for DOP metrics, and high values for πΆ/π0 .
2.3. Simulation Environment
In our previous works [4, 14], a detailed LEO constellation simulator has been developed in MATLAB
for LEO-PNT performance analysis, combining MATLAB libraries with the external QuaDRiGa channel
library [38], used for the link-budget modelling. Our simulator mimics a satellite constellation from a set
of inputs: the constellation parameters, the start time and the duration of the simulation, and the user
information (i.e., position and velocity vectors at each time instant and number of users placed according
to a uniform distribution on Earth). In addition, a secondary set of input parameters are provided for
QuaDRiGa models, which includes: satellite EIRP, receiver sensitivity, atmospheric attenuation effects,
and scenario information. We performed simulations for 2 different receiver sensitivity values; i) -125
dBm, representing the low-sensitivity case, and ii) -185 dBm, representing the high-sensitivity case,
under 6 different scenarios provided by the QuaDRiGa library that correspond to different Line of
Sight (LOS) and Non-Line of Sight (NLOS) conditions:
β’ "Indoor 1 (I-1)"; Indoor, Rural and NLOS
β’ "Indoor 2 (I-2)"; Indoor, Urban and NLOS
β’ "Outdoor 1 (O-1)"; Outdoor, Urban and NLOS
β’ "Outdoor 2 (O-2)"; Outdoor, Urban and LOS
β’ "Outdoor 3 (O-3)"; Outdoor, Rural and LOS
β’ "Outdoor 4 (O-4)"; Outdoor, Rural and NLOS
The simulations consider users uniformly spread on Earth and stationary, and rural/urban choice
changes the number of channel clusters and paths, as well as parameters related to large-scale fading
decorrelation distances and inter-parameter correlations within QuaDRiGa. Indoor scenarios are
considered with 50 meter penetration. Constellations are initialized according to their own parameters
as in Table 1. Each simulation has a duration of 1 hour with 1 minute samples, meaning 600 Monte-Carlo
runs per satellite in the constellation. Scenarios assume summer conditions for the atmospheric models.
3 attenuation models are taken into consideration in the link budget; i) atmospheric absorption, ii) rain,
and iii) fog, all calculated via MATLABβs internal functions. We assume light rain with 2.5 mm/h rate
and a cloud liquid water density of 0.5 g/m3. Temperature (π ), dry air pressure (π) and water-vapor
density (π) are modeled from the satellite altitude (β), as given in equations (2), (3) and (4) respectively
[39].
π (β) = 286.8374 β 4.7805β β 0.1402β2 [πΎ] (2)
π(β) = 1008.0278 β 113.2494β + 3.9408β2 [βπ π] (3)
2
π(β) = 8.988 exp(β03614β β 0.005402β β 0.001955β ) 3
[π/π3] (4)
3. Comparative Results and Discussion
Tables 2 and 3 show the results of the simulations for high and low sensitivity cases respectively,
providing LEO-PNT metrics averaged over the Monte-Carlo runs. The immediate thing to notice
between the two tables is the coverage difference; as expected, the coverage is significantly higher
in high-sensitivity than in low-sensitivity case for all constellations. Further examining the coverage
values, we see that:
i In Table 3, the cases with 0% 4-fold coverage show the GDOP, PDOP, πΆ/π0 and the received
power values as N/A, meaning not applicable, as DOP metrics have no physical meaning outside of
instances where PNT solutions exist.
ii In instances where the coverage does not change between Tables 2 and 3, the average πΆ/π0 and
received power values are the same (i.e., Starlink O-2), but for instances that change (i.e., Starlink
O-1), the better coverage instances include more conditions with weak signals, thus both the average
πΆ/π0 and received power values decrease.
iii It can be seen from Table 3 that Starlink, Xona and Centispace are the constellations that provide
resilience to indoor and low sensitivity conditions, and are still able to provide acceptable DOP
metrics given the four-fold coverage is achieved. In the less challenging scenarios (O-2, O-3 and
O-4), every LEO constellation is able to achieve acceptable metrics in low sensitivity conditions,
with Starlink, OneWeb and Xona being able to achieve full coverage. In comparison, Galileo is able
to either operate fully, as seen in O-2 and O-3 scenarios, or not able to operate at all due to the low
sensitivity of the receivers.
iv Table 2 shows a different picture, that even in high-sensitivity conditions, it can be challenging
to achieve a four-fold coverage. Centispace and the experimental designs show a similar picture;
they are able to achieve acceptable LEO-PNT metrics with a relatively low number of LEO satellites,
yet they struggle to achieve a complete four-fold coverage; while Xonaβs design seems to be better
performing but still cannot guarantee complete four-fold coverage in all scenarios. Among the
mega-constellations, Starlink and OneWeb achieve the best performance and are very similar, with
Kuiper falling behind; providing a better LEO-PNT performance compared to the smaller LEO
constellations, but failing to achieve a complete four-fold coverage in any scenario. Comparing the
rest of Table 2 to the Galileo entry, which serves as the GNSS benchmark, it is clear that similar,
if not better, PNT performance can be achieved with the considered LEO constellation designs in
comparison.
Galileo values serve as a benchmark and show what metrics are obtained with the current GNSS systems
in the considered scenarios, as well as what potential a LEO-PNT constellation has. Values in Table 2 in
particular, as the sensitivity allows for very high coverage for almost all designs, show what the main
appeal of a dedicated LEO-PNT constellation is over the GNSS constellations; a significant improvement
in πΆ/π0 for the same frequency band, and therefore, the possibility of shifting operations to higher
frequency bands, which are less crowded. While the best performing LEO constellation in this study is
indeed Starlink, we would like to emphasize that this does not strictly mean that a mega-constellation
is necessary to achieve good LEO-PNT services. In fact, the four-fold coverage difference seen between
Kuiper, Xona and Centispace shows that simply increasing the number of satellites within a constellation
does not directly translate into improved PNT performance. The smaller scale LEO-PNT constellations
and the author teamβs experimental designs provide a more balanced approach between cost/complexity
of the constellation and its PNT performance. Their πΆ/π0 is a direct improvement compared to Galileo,
and the DOP metrics, while slightly worse, are still within the same performance range of < 10.
We would like to further demonstrate two aspects via Fig. 1. The first aspect is that, Similar trends
are seen for both I-1 and O-2 scenarios. Indoor and outdoor conditions affect the constellations in the
same way by lowering their πΆ/π0 values drastically; which can impact the coverage depending on
receiver sensitivity levels. Fig. 1a and 1b show the LEO-PNT metrics of the considered constellations
with respect to each other for the high-sensitivity case, for two selected scenarios; I-1 and O-2. Looking
at Fig. 1a and 1b, Starlink and Oneweb yields drastically better πΆ/π0 and GDOP than Galileo while
equally maintaining the complete coverage, and Kuiper showing slightly worse πΆ/π0 and GDOP
compared to the other mega-constellations, yet fails to keep up with the complete coverage provided
by Galileo. Aside from the mega-constellations, Xona, Centispace and the experimental designs also
provide πΆ/π0 improvements over Galileo. Xona yields a slight improvement with respect to the GDOP,
while Centispace and the experimental designs have slightly worse GDOP compared to Galileo, but still
remain within good GDOP value ranges. We can also see that the average number of visible satellites
are higher compared to Galileo for the mega-constellations, Xona, and the experimental designs, which
(a) I-1 for -185 dBm (c) GDOP in I-1 for -185 dBm
(b) O-2 for -185 dBm (d) πΆ/π0 in I-1 for -185 dBm
Figure 1: Left: LEO-PNT metrics for all constellations; Right: GDOP and πΆ/π0 versus carrier frequency in I-1
imply better stability in cases of satellite failures.
The second aspect is the carrier frequency effect on LEO-PNT. Fig. 1c and 1d presents GDOP and
πΆ/π0 respectively for a subset of four constellations; Galileo, Oneweb, Centispace, and Experimental 1;
representing the GNSS benchmark, mega-constellations, small-scale LEO constellations, and potential
LEO-PNT constellations respectively. Scenario I-1 is shown as an example, with the observation that
similar trends have been noticed for O-2 scenario as well. The GDOP is not influenced much by the
carrier frequency, with the exception of Galileo, whose signals fail to penetrate the indoor scenario
with enough strength to be received by the receiver after around 20 GHz. On the other hand, πΆ/π0
drops in a consistent manner as for all constellations as the carrier frequency increases. Together, this
shows us that going above X-band or Ku-band for the carrier frequency would risk operational failure
for GNSS systems, while LEO-PNT systems would be able to support up to Ka-band carrier frequencies.
We note that LEO constellations operating in K-band can reach to the same πΆ/π0 values that the
GNSS constellations have in L-band, and that if the same carrier frequency is used, the improvement is
significant in favor of the LEO constellations versus Galileo, and GNSS by extension.
4. Conclusion
This article has shown an extensive comparison between seven LEO satellite constellations and Galileo
constellation as the GNSS benchmark (provided in Table 1) in terms of LEO-PNT metrics. We selected
the coverage, the GDOP, and the πΆ/π0 as relevant LEO-PNT performance metrics to be in used
comparisons. Using a MATLAB simulation created in-house for analysis, Tables 2 and 3 detail the
impact of different scenarios and receiver sensitivities on these performance metrics. Indoor/outdoor,
rural/urban, and LOS/NLOS scenarios are considered in the link budget, and compared for low (i.e.,
-125 dBm) versus high (i.e., -185 dBm) receiver sensitivity cases. We show that, among the available and
upcoming LEO constellations, Starlink, OneWeb, and Xona are the most promising for future LEO-PNT
applications. We also show that experimental designs that can reach similar GDOP and coverage
performance as Galileo (and GNSS by extension) are possible, which provide a direct improvement
on πΆ/π0 . This fact also hints at the possibility that alternative LEO-PNT constellation designs can be
further optimized for even better LEO-PNT solutions.
Acknowledgments
This work was supported by the Jane and Aatos Erko Foundation and by Teknologiateollisuus 100-year
Foundation, under the project INCUBATE. The Authors also thank Prof. B. Eissfeller, from University
of the Bundeswehr Munich for his constructive feedback on our LEO-PNT-related research in the team,
and Dr. R. Morales-Ferre, for developing parts of the custom simulator used in this work.
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Table 2
LEO-PNT Performance Metrics for β185 dBm Receiver Sensitivity (high sensitivity)
Coverage [%]
Constellation Scenario GDOP [-] PDOP [-] πΆ/π0 [dB-Hz] Received Power [dB] Path-Loss [dB]
4-Fold 1-Fold
I-1 100 100 3.88 3.34 8.67 β160.30 226.88
I-2 94.77 96.94 4.04 3.48 0.64 β168.34 236.43
O-1 100 100 3.86 3.31 24.80 β144.18 210.68
Galileo
O-2 100 100 3.85 3.31 50.66 β118.32 184.82
O-3 100 100 3.85 3.31 51.07 β117.91 184.41
O-4 100 100 3.85 3.31 33.93 β135.04 201.54
I-1 99.23 99.25 1.18 1.05 14.39 β154.59 238.53
I-2 96.48 96.88 1.32 1.17 3.27 β165.70 252.01
O-1 99.27 99.29 1.18 1.05 35.27 β133.71 217.21
Kuiper
O-2 99.27 99.29 1.17 1.04 64.05 β104.93 188.43
O-3 99.27 99.29 1.18 1.05 64.34 β104.63 188.13
O-4 99.27 99.29 1.18 1.04 48.37 β120.61 204.11
I-1 99.58 99.63 0.61 0.54 9.55 β159.43 233.00
I-2 93.15 93.92 0.63 0.56 0.45 β168.53 244.89
O-1 100 100 0.60 0.53 28.85 β140.12 212.62
OneWeb
O-2 100 100 0.60 0.53 56.00 β112.97 185.47
O-3 100 100 0.60 0.53 56.32 β112.66 185.16
O-4 100 100 0.60 0.53 40.96 β128.01 200.51
I-1 100 100 0.89 0.80 19.16 β149.81 226.59
I-2 99.38 99.46 0.90 0.81 7.58 β161.40 239.21
O-1 100 100 0.86 0.77 39.40 β129.58 206.18
Starlink
O-2 100 100 0.84 0.75 66.43 β102.55 179.15
O-3 100 100 0.83 0.73 66.73 β102.25 178.85
O-4 100 100 0.84 0.74 51.53 β117.45 194.05
I-1 96.77 100 5.94 5.31 40.70 β128.27 200.77
I-2 96.77 100 5.93 5.29 32.28 β136.70 209.20
O-1 96.77 100 5.93 5.31 57.57 β111.41 183.91
Centispace
O-2 96.77 100 5.95 5.31 78.89 β90.09 162.59
O-3 96.77 100 5.94 5.31 79.24 β89.73 162.23
O-4 96.77 100 5.93 5.31 65.39 β103.59 176.09
I-1 100 100 3.03 2.71 40.49 β128.49 200.99
I-2 100 100 3.05 2.71 32.32 β136.65 209.16
O-1 100 100 3.03 2.71 57.31 β111.67 184.17
Xona
O-2 100 100 3.05 2.73 78.53 β90.44 162.94
O-3 96.9 100 3.04 2.72 78.89 β90.09 162.59
O-4 100 100 3.04 2.72 65.11 β103.86 176.36
I-1 99.71 100 5.36 4.77 14.94 β154.03 230.73
I-2 93.10 95.48 5.27 4.67 3.78 β165.19 242.98
O-1 99.98 100 5.37 4.76 34.84 β134.13 210.73
Experimental 1
O-2 100 100 5.37 4.78 62.09 β106.89 183.49
O-3 100 100 5.37 4.78 62.40 β106.57 183.17
O-4 99.98 100 5.36 4.76 47.07 β121.90 198.50
I-1 99.19 100 5.98 5.36 18.34 β150.64 227.32
I-2 92.5 97.15 5.89 5.27 7.20 β161.77 240.10
O-1 99.71 100 5.91 5.32 37.06 β131.91 208.51
Experimental 2
O-2 99.75 100 5.92 5.33 63.95 β105.03 181.63
O-3 99.75 100 5.93 5.33 64.25 β104.72 181.32
O-4 99.75 100 5.94 5.32 49.19 β119.78 196.38
Table 3
LEO-PNT Performance Metrics for β125 dBm Receiver Sensitivity (low sensitivity)
Coverage [%]
Constellation Scenario GDOP [-] PDOP [-] πΆ/π0 [dB-Hz] Received Power [dB] Path-Loss [dB]
4-Fold 1-Fold
I-1 0 0.38 N/A N/A N/A N/A 226.88
I-2 0 0.38 N/A N/A N/A N/A 236.43
O-1 0 0.38 N/A N/A N/A N/A 210.68
Galileo
O-2 100 100 3.86 3.31 50.66 β118.32 184.82
O-3 100 100 3.85 3.30 51.07 β117.91 184.41
O-4 0 0.60 N/A N/A N/A N/A 201.54
I-1 0 3.21 N/A N/A N/A N/A 238.53
I-2 0 1.19 N/A N/A N/A N/A 252.01
O-1 0 5.02 N/A N/A N/A N/A 217.21
Kuiper
O-2 99.27 99.29 1.18 1.05 64.05 β104.93 188.43
O-3 99.27 99.29 1.18 1.05 64.34 β104.63 188.13
O-4 99.27 99.29 1.28 1.14 48.38 β120.60 204.11
I-1 0 2.15 N/A N/A N/A N/A 233.00
I-2 0 1.67 N/A N/A N/A N/A 244.89
O-1 0 2.23 N/A N/A N/A N/A 212.62
OneWeb
O-2 100 100 0.60 0.53 56.00 β112.97 185.47
O-3 100 100 0.60 0.53 56.32 β112.66 185.16
O-4 100 100 3.46 2.72 45.39 β123.58 200.51
I-1 5.81 9.96 4.17 3.34 46.80 β122.18 226.59
I-2 0 1.94 N/A N/A N/A N/A 239.11
O-1 80 81.52 3.47 2.79 45.75 β123.22 206.18
Starlink
O-2 100 100 0.87 0.78 66.43 β102.55 179.15
O-3 100 100 0.86 0.77 66.73 β102.25 178.85
O-4 100 100 0.91 0.82 51.55 β117.43 194.05
I-1 33.04 70.35 7.67 6.83 51.08 β117.90 200.77
I-2 8.06 34.27 5.68 4.86 48.06 β120.92 209.20
O-1 96.52 100 5.97 5.28 57.59 β111.39 183.91
Centispace
O-2 96.77 100 5.93 5.31 78.89 β90.09 162.59
O-3 96.77 100 5.93 5.31 79.24 β89.73 162.23
O-4 96.77 100 5.88 5.27 65.39 β103.59 176.09
I-1 55.65 72.92 5.91 5.12 49.99 β118.98 200.99
I-2 19 40.77 8.33 7.26 48.16 β120.81 209.16
O-1 100 100 3.14 2.78 57.34 β111.64 184.17
Xona
O-2 100 100 3.03 2.71 78.53 β90.44 162.94
O-3 100 100 3.03 2.71 78.89 β90.09 162.59
O-4 100 100 3.05 2.72 65.11 β103.86 176.36
I-1 0 0 N/A N/A N/A N/A 230.73
I-2 0 0 N/A N/A N/A N/A 242.98
O-1 0 0 N/A N/A N/A N/A 210.73
Experimental 1
O-2 99.98 100 5.37 4.78 62.09 β106.89 183.49
O-3 99.98 100 5.37 4.78 63.12 β106.57 183.17
O-4 87.67 100 5.27 4.72 47.05 β121.92 198.50
I-1 0 2.56 N/A N/A N/A N/A 227.32
I-2 0 0.04 N/A N/A N/A N/A 241.10
O-1 0 8.23 N/A N/A N/A N/A 208.51
Experimental 2
O-2 99.67 100 5.94 5.35 63.95 β105.03 181.63
O-3 99.67 100 5.93 5.35 64.25 β104.72 181.32
O-4 91.52 100 6.08 5.40 49.19 β119.78 196.38