  We present a new calibration scheme based on a non-linear version of Kalman
filter that aims at estimating the physical terms appearing in the Radio
Interferometry Measurement Equation (RIME). We enrich the filter's structure
with a tunable data representation model, together with an augmented
measurement model for regularization. We show using simulations that it can
properly estimate the physical effects appearing in the RIME. We found that
this approach is particularly useful in the most extreme cases such as when
ionospheric and clock effects are simultaneously present. Combined with the
ability to provide prior knowledge on the expected structure of the physical
instrumental effects (expected physical state and dynamics), we obtain a fairly
cheap algorithm that we believe to be robust, especially in low signal-to-noise
regime. Potentially the use of filters and other similar methods can represent
an improvement for calibration in radio interferometry, under the condition
that the effects corrupting visibilities are understood and analytically
stable. Recursive algorithms are particularly well adapted for pre-calibration
and sky model estimate in a streaming way. This may be useful for the SKA-type
instruments that produce huge amounts of data that have to be calibrated before
being averaged.
