Kalman filtering in two dimensions

Authors:
J. Woods;C. Radewan
Affiliations:
-;-
Venue:
IEEE Transactions on Information Theory
Year:
2006

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Abstract

The Kalman filtering method is extended to two dimensions. The resulting computational load is found to be excessive. Two new approximations are then introduced. One, called the strip processor, updates a line segment at a time; the other, called the reduced update Kalman filter, is a scalar processor. The reduced update Kalman filter is shown to be optimum in that it minimizes the post update mean-square error (mse) under the constraint of updating only the nearby previously processed neighbors. The resulting filter is a general two-dimensional recursive filter.