GNSS Constellation Specific Monthly Analysis Summary: November 2024

The analysis performed in this report is solely his work and own opinion. State Program: U.S.A (G); EU (E); China (C) “Only MEO- SECM satellites”; Russia (R); Japan (J); India (I)

Narayan Dhital Actively involved to support international collaboration in GNSSrelated activities. He has regularly supported and contributed to different workshops of the International Committee on GNSS (ICG), and the United Nations Office for Outer Space Affairs (UNOOSA). As a professional employee, the author is working as GNSS expert at the Galileo Control Center, DLR GfR mbH, Germany

Introduction

The article is a continuation of monthly performance analysis of the GNSS constellation. Please refer to previous issues for past analysis. The time transfer method using GNSS pseudorange measurements is further analyzed in this month’s analysis. An example of the application of GNSS for on-board orbit determination and time synchronization in LEO missions is provided.

Analyzed Parameters for November, 2024

(Dhital et. al, 2024) provides a brief overview of the necessity and applicability of monitoring the satellite clock and orbit parameters.
a.. Satellite Broadcast Accuracy, measured in terms of Signal-InSpace Range Error (SISRE) (Montenbruck et. al, 2010).
b. SISREOrbit ( only orbit impact on the range error), SISRE (both orbit and clock impact), and SISREPPP (as seen by the users of carrier phase signals, where the ambiguities absorb the unmodelled biases related to satellite clock and orbit estimations. Satellite specific clock bias is removed)
c. Clock Discontinuity: The jump in the satellite clock offset between two consecutive batches of data uploads from the ground mission segment. It is indicative of the quality of the satellite atomic clock and associated clock model.
d. URA: User Range Accuracy as an indicator of the confidence on the accuracy of satellite ephemeris. It is mostly used in the integrity computation of RAIM.
e. GNSS-UTC offset: It shows stability of the timekeeping of each constellation w.r.t the UTC.
f. 1 PPS: 1 Pulse Per Second signal is a highly accurate timing reference generated by GNSS receiver, providing a precise pulse every second for synchronization and timekeeping applications.

Note:- for India’s IRNSS there are no precise satellite clocks and orbits as they broadcast only 1 frequency which does not allow the dual frequency combination required in precise clock and orbit estimation; as such, only URA and Clock Discontinuity is analyzed.

(f) GNSS 1 PPS Signal for IoT Devices

IoT devices are ubiquitous in the modern world, enabled by the miniaturization and low cost of sensors, which allow interconnected devices and services to operate seamlessly. A crucial component in this ecosystem is the 1 PPS signal. The combination of the 1 PPS signal and GPS UTC time provides precise time tagging for various sensors and serves as a synchronization medium for a network of sensors. Most devices incorporate an internal oscillator (crystal or TCXO) for timekeeping, offering reasonable accuracy, typically within a minute per year. However, multiple time sources, including LTE cell networks, NTP, and GNSS time, are often utilized. One commonly used RTC in sensors and devices is the DS3231, which features an integrated crystal oscillator and temperature compensation, making it less susceptible to temperature variations.

Using the GT U7 GPS module with an Arduino UNO R3 microcontroller for GNSS time applications involves leveraging the 1 PPS signal for precise time tagging and synchronization of sensors in IoT devices. The 1 PPS signal provides a highly accurate timing reference, which can be used to synchronize the internal clocks of various sensors, ensuring consistent data collection. By connecting the 1 PPS output of the GT U7 GPS module to an interrupt pin on the Arduino, the time between pulses can be measured to maintain synchronization. This method is particularly useful in the absence of Network Time Protocol (NTP) servers, as the GPS 1 PPS signal serves as a reliable time source, often used in NTP stratum 1 servers.

In this demonstration, the setup is illustrated in Figure f(a). The RTC and GT U7 GPS module are both connected to the microcontroller through data pins, with the Arduino IDE used for control and code execution. The bottom left of Figure f(a) shows the steering of the RTC using the GPS 1 PPS signal. Initially, the RTC was set to a random time, and after a few seconds, steering was initiated via a control command. The RTC time was immediately synchronized to the GPSderived CET time. Due to processing delays and resource overload on the microcontroller, the printed output (for demonstration purposes) of the RTC time and the CET time shown on the internet may not match, but they are synchronized in principle. After a few minutes, the steering was stopped, and the RTC was left to run freely. After 10 days, the RTC time was compared against the UTC time, as shown in the top right of Figure f(a). The RTC time had advanced by more than 2 seconds, consistent with the reported accuracy of 2 ppm forDS3231 oscillator (approximately 1 minute per year).

Regarding the 1 PPS signal, the Arduino interrupt service routine reported slight variations in the 1 PPS timing (e.g., 1000 ms, 999 ms, 1001 ms) of the rising edge from the digital pin. These discrepancies can be attributed to factors such as inherent jitter in the GPS signal and processing delays within the Arduino. A jitter of 1 to 10 ms, as shown in Figure f(b), is relatively high and not typical for most GPS modules, where jitter is usually in the range of nanoseconds to microseconds. Additionally, GPS satellite visibility and signal obstruction can affect the stability of the 1 PPS signal. The setup in Figure f(a) indicates poor signal quality in the surrounding environment. Furthermore, the microcontroller may introduce additional delays due to its processing limitations. The goal of this demonstration is to showcase the synchronization of the RTC module with the 1 PPS signal, ensuring that the RTC maintains reliable time for the majority of IoT sensors, even when the GPS signal is unavailable or degraded.

Note: as the above demonstration is only meant for an information purpose on the application of GNSS timing on IoT sensors, no references are mentioned. The used hardware was randomly selected based on the availability. There are plenty of materials on the internet related to 1 PPS based timing for such applications.

Monthly Performance Remarks:
1. Satellite Clock and Orbit Accuracy:
▪ Except Beidou and Galileo, degradation in performances is noticed for other constellation.
▪ For GPS, the clock performances look degraded for multiple satellites on different days.
▪ For QZSS, the performance of orbits is highly degraded for the time 318-323 day of year.
▪ For IRNSS, URA value distribution for all satellites shows low spread than before.

2. UTC Prediction (GNSS-UTC):
▪ All constellations show stable UTC prediction with minor variations. GPS and Galileo both provided slightly diverging values on couple of occasions. GLONASS started to diverge at the end of the month.

References

Alonso M, Sanz J, Juan J, Garcia, A, Casado G (2020) Galileo Broadcast Ephemeris and Clock Errors Analysis: 1 January 2017 to 31 July 2020, MDPI

Alonso M (2022) Galileo Broadcast Ephemeris and Clock Errors, and Observed Fault Probabilities for ARAIM, Ph.D Thesis, UPC

BIMP (2024 a) https://e-learning.bipm. org/pluginfile.php/6722/mod_label/ intro/User_manual_cggtts_analyser. pdf?time=1709905608656

BIMP (2024 b) https://e-learning. bipm.org/mod/folder/view. php?id=1156&forceview=1

BIMP (2024 c) https://cggttsanalyser.streamlit.app

Cao X, Zhang S, Kuang K, Liu T (2018) The impact of eclipsing GNSS satellites on the precise point positioning, Remote Sensing 10(1):94

Dhital N (2024) GNSS constellation specific monthly analysis summary, Coordinates, Vol XX, Issue 1, 2, 3, 4

Hauschlid A, Montenbruck O (2020) Precise real-time navigation of LEO satellites using GNSS broadcast ephemerides, ION

Guo F, Zhang X, Wang J (2015) Timing group delay and differential code bias corrections for BeiDou positioning, J Geod, IERS C04 (2024) https://hpiers.obspm.fr/ iers/eop/eopc04/eopc04.1962-now

IGS (2021) RINEX Version 4.00 https://files.igs.org/pub/data/ format/rinex_4.00.pdf

Li M, Wang Y, Li W (2023) performance evaluation of real-time orbit determination for LUTAN-01B satellite using broadcast earth orientation parameters and multiGNSS combination, GPS Solutions, Vol 28, article number 52

Li W, Chen G (2023) Evaluation of GPS and BDS-3 broadcast earth rotation parameters: a contribution to the ephemeris rotation error

Liu T, Chen H, Jiang Weiping (2022) Assessing the exchanging satellite attitude quaternions from CNES/ CLS and their application in the deep eclipse season, GPS Solutions 26(1)

Montenbruck O, Steigenberger P, Hauschlid A (2014) Broadcast versus precise ephemerides: a multiGNSS perspective, GPS Solutions

Montenbruck O, Hauschlid A (2014 a) Differential Code Bias Estimation using Multi-GNSS Observations and Global Ionosphere Maps, ION

Steigenberger P, Montenbruck O, Bradke M, Ramatschi M (2022) Evaluation of earth rotation parameters from modernized GNSS navigation messages, GPS Solutions 26(2)

Sylvain L, Banville S, Geng J, Strasser S (2021) Exchanging satellite attitude quaternions for improved GNSS data processing consistency, Vol 68, Issue 6, pages 2441-2452

Walter T, Blanch J, Gunning K (2019) Standards for ARAIM ISM Data Analysis, ION

Wang N, Li Z, Montenbruck O, Tang C (2019) Quality assessment of GPS, Galileo and BeiDou-2/3 satellite broadcast group delays, Advances in Space Research

Wang J, Huang S, Lia C (2014) Time and Frequency Transfer System Using GNSS Receiver, Asia-Pacific Radio Science, Vol 49, Issue 12

https://cggtts-analyser.streamlit.app

Note: References in this list might also include references provided to previous issues.

Data sources and Tools:

https://cddis.nasa.gov (Daily BRDC); http://ftp.aiub.unibe.ch/ CODE_MGEX/CODE/ (Precise Products); BKG “SSRC00BKG” stream; IERS C04 ERP files

(The monitoring is based on following signals- GPS: LNAV, GAL: FNAV, BDS: CNAV-1, QZSS:LNAV IRNSS:LNAV GLO:LNAV (FDMA))

Time Transfer Through GNSS Pseudorange Measurements: https://elearning.bipm.org/login/index.php Allan Tools, https://pypi.org/project/ AllanTools/gLAB GNSS, https:// gage.upc.edu/en/learning-materials/ software-tools/glab-tool-suite