Kalman Filter-based Robust Closed-loop Carrier Tracking of Airborne GNSS Radio-Occultation Signals

Area of Science:

• Atmospheric science
• Geophysics
• Signal processing

Background:

• Global Navigation Satellite System (GNSS) radio occultation (RO) is a key technique for retrieving atmospheric profiles.
• Open-loop (OL) processing is standard for GNSS-RO, but struggles with signal scintillation in the lower troposphere.
• Multipath effects and surface reflections degrade low-elevation GNSS signals, causing amplitude fading and phase fluctuations.

Purpose of the Study:

• To introduce an adaptive Kalman filter-based closed-loop (KFC) tracking method for GNSS-RO signal processing.
• To address limitations of OL tracking, particularly in challenging lower tropospheric conditions.
• To enhance the accuracy and robustness of atmospheric profile retrieval from GNSS-RO data.

Main Methods:

• Developed an adaptive Kalman filter-based closed-loop (KFC) tracking algorithm.
• Incorporated tropospheric scintillation, platform vibration, and real-time C/N0 estimation into the KFC method.
• Validated the KFC method using real GPS RO data from an airborne platform.

Main Results:

• The KFC method demonstrated comparable robustness and improved accuracy over traditional OL tracking.
• Analysis of excess Doppler estimation, bending angles, and impact parameters confirmed KFC's superior performance.
• The proposed algorithm effectively mitigates signal scintillation effects.

Conclusions:

• The adaptive KFC tracking method offers a significant advancement for GNSS-RO data processing.
• This technique enhances the reliability of atmospheric profile retrieval, especially in the lower troposphere.
• KFC tracking provides a more accurate and robust alternative to OL processing for GNSS-RO applications.