  Kalman filtering is a widely used framework for Bayesian estimation. The
partitioned update Kalman filter applies a Kalman filter update in parts so
that the most linear parts of measurements are applied first. In this paper, we
generalize partitioned update Kalman filter, which requires the use oft the
second order extended Kalman filter, so that it can be used with any Kalman
filter extension. To do so, we use a Kullback-Leibler divergence approach to
measure the nonlinearity of the measurement, which is theoretically more sound
than the nonlinearity measure used in the original partitioned update Kalman
filter. Results show that the use of the proposed partitioned update filter
improves the estimation accuracy.
