GNSS‐ISR data fusion: General framework with application to the high‐latitude ionosphere
A mathematical framework is presented for the fusion of electron density measured by incoherent scatter radar (ISR) and total electron content (TEC) measured using global navigation satellite systems (GNSS). Both measurements are treated as projections of an unknown density field (for GNSS-TEC the projection is tomographic; for ISR the projection is a weighted average over a local spatial region) and discrete inverse theory is applied to obtain a higher fidelity representation of the field than could be obtained from either modality individually. The specific implementation explored herein uses the interpolated ISR density field as initial guess to the combined inverse problem, which is subsequently solved using maximum entropy regularization. Simulations involving a dense meridional network of GNSS receivers near the Poker Flat ISR demonstrate the potential of this approach to resolve sub-beam structure in ISR measurements. Several future directions are outlined, including (1) data fusion using lower level (lag product) ISR data, (2) consideration of the different temporal sampling rates, (3) application of physics-based regularization, (4) consideration of nonoptimal observing geometries, and (5) use of an ISR simulation framework for optimal experiment design.