Abstract
The
Mueller matrix of an optical instrument describes the polarimetric
effects the instrument will have on the optical observations it makes
in terms of the Stokes parameters. The calibration of the instrument
relies on a robust characterization of the elements of this matrix. In
this paper, we present what we believe is a new technique that uses
Kalman filtering to characterize the Mueller matrices of optical
instrumentation based on a set of lab calibration measurements. Kalman
filtering is a ubiquitous statistical optimizer that works by
comparing measurements and a model of the observed physical system to
minimize error. Typically, this technique is applied as a filter to
refine a set of observations, but it can also be used to retrieve the
properties of the physical system that are not directly measured. We
demonstrate the use of the Kalman approach to polarimetric calibration
through simulation of measurements, where the polarimetric behavior of
optical elements is represented by the Mueller matrices of individual
components. The elements of these Mueller matrices are then retrieved
with the uncertainty estimates using the Kalman filter approach. The
results of this simulation are compared to the standard polarimetric
calibration technique as a benchmark, demonstrating the superior
performance of the Kalman approach. Then, both the Kalman technique
and the standard technique are applied to real measurements from a
multispectral polarimetric imager used for atmospheric aerosol remote
sensing.