The article proposes a straightforward Kalman filter-based method for
computationally efficient ionospheric electron density multi-instrument
imaging. The approach uses direct ionospheric measurements, such as
ionosondes, and general physical assumptions to estimate the uncertainty
associated with the previous reconstructed time step. Therefore the
method does not require any electron density model of the ionosphere as
a background. The uncertainty is represented by an inverse covariance
matrix constructed with Gaussian Markov random fields, allowing the
problem to be solved numerically with relatively high resolution. The
experiments utilise measurements from dense ground-based GNSS and low
Earth orbit beacon satellite receiver networks as well as ionosondes. A
synthetic simulation study and real data validation with a specific
EISCAT incoherent scatter radar measurement campaign is carried out over
Northern European sector. The method can be controlled using parameters
with probabilistic and physically realistic interpretations that can be
applied to both simulated and real-world data. The results show that the
approach is feasible for near real-time regional ionospheric imaging.
Especially, the method can be seen as an expansion to local profile
measurements field of view, but with sufficient measurement coverage, it
also provides information further away from the specific instrument.