When I learned “Kalman filter” for the first time, I was not sure how to distinguish it from “Yule-Walker equation” (time series), “Pade approximant, (unfortunately, the wiki page does not have its matrix form). Wiener Filter” (signal processing), etc. Here are those publications, specifically mentioned the name Kalman filter in their abstracts found from ADS.

• Application of Data Assimilation Method for Predicting Solar Cycles (2008) in ApJ
• Time series analysis in astronomy: Limits and potentialities (2005) in A&A
  From the abstract: Only techniques of data aalysis developed in a specific physical context can be expected to provide useful results. The filed of stochastic dynamics appears to be an interesting framework for such an approach.
• Determination of the Mass of Jupiter Using the Motion of Its Ninth Satellite and a Kaiman-Bucy Filter (1972) in A&A

The motivation of introducing Kalman filter although it is a very well known term is the recent Fisher Lecture given by Noel Cressie at the JSM 2009. He is the leading expert in spatial statistics. He is the author of a very famous book in Spatial Statistics. During his presentation, he described challenges from satellite data and how Kalman filter accelerated computing a gigantic covariance matrix in kriging. Satellite data of meteorology and geosciences may not exactly match with astronomical satellite data but from statistical modeling perspective, the challenges are similar. Namely, massive data, streaming data, multi dimensional, temporal, missing observations in certain areas, different exposure time, estimation and prediction, interpolation and extrapoloation, large image size, and so on. It’s not just focusing denoising/cleaning images. Statisticians want to find the driving force of certain features by modeling and to perform statistical inference. (They do not mind parametrization of interesting metric/measure/quantity for modeling or they approach the problem in a nonparametric fashion). I understood the use of Kalman filter for a fast solution to inverse problems for inference.

The abstract has one sentence: There are none and the first paragraph of this short paper explains the uniqueness of the abstract:

Considerable attention has been given lately to the issue of robustness of linear-quadratic (LQ) regulators. The recent work by Safonov and Athans^[1] has extended to the multivariable case the now well-known guarantee of 60(deg) phase and 6dB gain margin for such controllers. However, for even the single-input, single-output case there has remained the question of whether there exist any guaranteed margins for the full LQG (Kalman filter in the loop) regulator. By counterexample, this note answers that question; there are none.

Quoting the whole abstract is an intention of little entertainment, whereas the content may not be of any interests to astronomers and statisticians. There is a long winding evolutionary history in Automatic Control, which has dragged many mathematicians, applied mathematicians, and statisticians (The earlier versions of Kalman filter, AIC, and MDL appeared in this journal).

1. M.G. Safonov and M. Athans (1977), IEEE Transactions on Automatic Control, 22 (Apr.), pp. 173-179