LAUSR

Global navigation satellite system radio occultation (GNSS-RO) signals have been demonstrated as a viable means to retrieve atmospheric profiles. Current GNSS-RO observations rely on open-loop (OL) processing of the signals, especially for signals propagating through the lower troposphere. The reason being GNSS signals at low elevations are adversely affected by multipath effects due to propagation through lower troposphere structures and reflections and scattering from the Earth surface. The low-elevation RO signals are characterized by deep and fast amplitude fading and rapid signal carrier phase fluctuations, collectively referred to as signal scintillation. The conventional phase-lock loop may lose lock of these signals. While OL tracking is known for its robustness, its accuracy is determined by the climatological models used to create the reference for the GNSS signal carrier tracking loop. The wide bandwidth typically associated with OL tracking also introduces large errors in signal parameters estimations. In this article, we present an adaptive Kalman filter-based closed-loop (KFC) tracking method, which takes into consideration the tropospheric scintillation, platform vibration, and real-time C/N$_0$ estimation of the RO signals. The KFC method has comparable robustness with and improved accuracy over the OL tracking, which are demonstrated through comparison using real global positioning system RO data collected on an airborne platform. Analysis of the excess Doppler estimation, retrieved bending angles, and impact parameters also confirms the improved performances of the proposed algorithm over OL tracking.