Sparse Reconstruction of Regional Ionospheric Tomography Based on Beidou Ground Based Augmentation System

Real-time and accurate modeling of three-dimensional (3D) electron density in the ionosphere is important for space navigation and communication technology as well as space weather. This paper investigates ionospheric tomography based on compressive sensing (CS) to achieve high resolution with sparse observation. This methodology includes the slant electron density (STEC) extraction by the un-difference and un-combined precise point positioning algorithm (UPPP), VTEC mapping based on basis function and compressive sensing reconstruction. The validation of the sparse sensing methods have been tested in both simulation and real navigation data in Yunnan from Beidou Ground Based Augmentation System (GBAS) by Qianxun Spatial Intelligence Inc. The constructed electron density by compressive sensing based on real GBAS navigation data agrees well with incoherent scatter radar (ISR) and ionosonde data in Qujing, Yunnan. The sparse sensing method shows 50% accuracy improvement in comparison to traditional ART method. In conclusion, the CS method can effectively reconstruct the three-dimensional electron density of the regional ionosphere by only relying on sparse observation, which can be used for real-time ionospheric tomography.