FR3124604A1 - Total Electronic Content Estimation and Prediction Process - Google Patents

Abstract

To allow better mapping of the ionosphere, the method uses dual-frequency GNSS receivers (110) on board aircraft (109). GNSS signals (111) are collected after passing through the ionosphere by the receiving antenna (201) of said GNSS receiver. Abstract figure: Fig. 1

Description

Total Electronic Content Estimation and Prediction Process

State of the prior art

Communications using radio frequencies between the Earth and space are impacted by scintillations from the ionosphere (106), the effects of which depend in particular on solar activity. Indeed, the Sun (104) emits high energy particles and radiation (105) which ionize a layer of the upper atmosphere called the thermosphere (an electrically neutral medium), it is this phenomenon of ionization which is origin of the ionosphere [1] (106).

Predicting variations in the ionosphere makes it possible to anticipate the unavailability of radio communications between the Earth and space or errors in satellite radionavigation [2].

In the case of radio navigation, the GNSS satellites (101) emit signals (102) towards the nadir intended for users (103) equipped with receivers which can determine their positions by triangulation thanks to the analysis of the signals emitted by at least four satellites [3].

However, the density variations of the ionosphere delay the signals (102) emitted by the GNSS satellites (101) which cross the ionosphere (107). The user's GNSS receiver on Earth (103) collects the incident GNSS signals (108) and measures by demodulation of the signals and by correlation between these signals with a replica of the expected signal, the pseudodistance between its position and the satellite visible from the GNSS-constellation. The propagation delay due to the ionosphere therefore introduces a measurement bias in this positioning process and therefore contributes to the navigation error [4].

To limit satellite positioning errors, those skilled in the art measure the density of the ionosphere by comparing phases between two signals transmitted simultaneously by a GNSS satellite and received by a receiver capable of simultaneously collecting and processing these signals [ 5]. The ionosphere is characterized by the total electronic content which represents the total number of electrons present along a tube of one square meter in section.

The ionosphere is characterized by electrically charged areas (ions or electrons), also called plasma. When density variations take place in this plasma, induced for example by day/night transitions, the refractive index of the medium evolves according to the Appleton-Hartee formula:

With electron density, the charge of an electron, the mass of an electron, the dielectric permittivity of vacuum and the the frequency of the wave propagating in the medium.

Therefore when an electromagnetic wave propagates in the medium it is refracted. This phenomenon of refraction depends on the frequency of the electromagnetic wave and induces a propagation delay, called ionospheric delay, which modifies the phase of the electromagnetic wave.

From the phase measurement of at least two signals emitted simultaneously and carried on different frequencies, those skilled in the art can estimate the refractive index and therefore the electron density of the zone of the ionosphere crossed by these signals [6].

However, the state of the art uses receivers located on the continents. The ionosphere is therefore not comprehensively characterized, limiting the accuracy of total electronic content prediction services.

Description of figures

Detailed embodiments

To allow better mapping of the ionosphere, the method uses dual-frequency GNSS receivers (110) on board aircraft (109). GNSS signals (111) are collected after passing through the ionosphere by the receiving antenna (201) of said GNSS receiver.

The reception antenna is interfaced (202) with the reception and demodulation circuits (203) which measure the phase of the signals on at least two frequencies (for example the GPS L1 and L2 frequencies). The reception and demodulation circuit dates and locates the phase measurements using a navigation solution known to those skilled in the art [7].

The reception and demodulation circuit is interfaced (205) with a terminal server (204) and transmits to it the dating and location information (206), the measurement of the phase (207) of the signal carried by the first frequency (for example L1) and the measurement of the phase (208) of the signal carried by the second frequency (for example L2).

According to another embodiment, the reception and demodulation circuit transmits to the terminal server phase measurements of signals carried on more than two frequencies.

The terminal server transmits this data by wireless link (209) to a second terminal server (211) equipped with a wireless link (210).

According to another embodiment, the terminal server encrypts or certifies the data before transmitting it.

The second terminal server is interfaced to the ISSAN platform (213) by means of a network (212) (for example the Internet).

The ISSAN platform aggregates data (301) from said GNSS receivers but also solar and geomagnetic indices that reflect solar activity [Add references].

A Machine Learning algorithm (308) uses the data aggregated by ISSAN including the phase measurements of the signals carried by the first frequency (305) and the phase measurements of the signals carried by the second frequency (306) which are dated and localized ( 304). The Machine Learning algorithm is implemented according to [8].

In another embodiment, the machine learning algorithm also uses solar indices (302) and geomagnetic indices (303).

The Machine Learning algorithm estimates or predicts the Total Electronic Content (402) based on location and date (401). The global coverage of ionospheric density measurements allows for improved estimation and prediction.

Claim the use of phase measurements, dated and localized, of GNSS signals emitted simultaneously by a space station and received by an aircraft equipped with a multi-frequency receiver to estimate and predict the Total Electronic Content.

Generalize to other multi-frequency GNSS receivers (Carriers = ships, cars, telephony, etc.).

Generalize to any station that transmits signals on different frequencies.

Generalize to emissions from Earth to space or from space to Earth.

Generalize the Machine Learning algorithm to the use of other solar and geomagnetic indices to improve accuracy.

Bibliography

[1] Bothmer, V., & Daglis, I.A. (2007). Space weather: physics and effects. Springer Science & BusinessMedia.

[2] ITU (2019). Recommendation ITU-R P.531-14: Ionospheric propagation data and prediction methods required for the design of satellite networks and satellite systems

[3] Elliott Kaplan and Christopher J. Hegarty. 2017. Understanding GPS/GNSS: Principles and Applications, Third Edition (3rd ed.). Artech House, Inc., USA.

[4] McGraw, Gary A., Murphy, Tim, Brenner, Mats, Pullen, Sam, Van Dierendonck, A. J., "Development of the LAAS Accuracy Models," Proceedings of the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2000), Salt Lake City, UT, September 2000, p. 1212-1223.

[5] Michal, Debra P., Klotz, Harold A., Jr., Hoven, Randall M., "A Systems Approach to Ionospheric Delay Compensation," Proceedings of IEEE/ION PLANS 2006, San Diego, CA, April 2006, p.p. 275-282.

[6] Mannucci, A. J., Wilson, B. D., Yuan, D. N., Ho, C. H., Lindq-Wister, U. J., and Runge, T. F. (1998). A global mapping technique for gps-derived ionospheric total electron content measurements. Radio Science, 33(3):565–582.

[7] E. D. Kaplan and C. J. Hegarty, “Understanding GPS: Principles and Applications,” 3rd Edition, Artech House Inc., Norwood, 2017.

[8] Cheng, N.; Song, S.; Li, W. Multi-Scale Ionospheric Anomalies Monitoring and Spatio-Temporal Analysis during Intense Storm. Atmosphere 2021, 12, 215. https://doi.org/10.3390/atmos12020215

Claims (1)

- Claim the use of phase measurements, dated and localized, of GNSS signals emitted simultaneously by a space station and received by an aircraft equipped with a multi-frequency receiver to estimate and predict the Total Electronic Content.
Generalize to other multi-frequency GNSS receivers (Carriers = ships, cars, telephony, etc).
Generalize to any station that transmits signals on different frequencies.
Generalize to emissions from Earth to space or from space to Earth.
Generalize the Machine Learning algorithm to the use of other solar and geomagnetic indices to improve accuracy.