An improved method using adaptive smoothing for GNSS tomographic imaging of ionosphere

Area of Science:

• Space Physics
• Geophysics
• Atmospheric Science

Background:

• Global Navigation Satellite System (GNSS) is crucial for ionosphere research, providing reliable Total Electron Content (TEC) data.
• Traditional GNSS ionospheric tomography is sensitive to sparse and non-uniform station distribution, limiting accuracy.
• A priori information significantly impacts the performance of existing tomography techniques.

Purpose of the Study:

• To develop an improved GNSS ionospheric tomography method reducing reliance on a priori information.
• To enhance the accuracy and reliability of ionospheric electron density reconstruction.
• To validate the proposed method using simulated and real-world observation data.

Main Methods:

• An improved method based on adaptive Laplacian smoothing and algebraic reconstruction technique (ALS-ART) was developed.
• The ALS-ART method utilizes adaptive smoothing constraints, making it less dependent on a priori information.
• Simulated and real GNSS observation data were used for tomography experiments and validation.

Main Results:

• Tomography experiments with simulated data showed significantly improved ionospheric electron density reconstruction accuracy using ALS-ART.
• Analysis of real observation data demonstrated the superiority of ALS-ART compared to traditional methods.
• The proposed method shows improved reliability in ionosphere TEC estimation.

Conclusions:

• The ALS-ART method offers enhanced accuracy and reliability for GNSS ionospheric tomography.
• This technique reduces the dependency on a priori information, making it more adaptable.
• The improved method will advance the capability of obtaining precise ionosphere TEC data using GNSS.