  In this paper we present a new Kalman filter extension for state update
called Partitioned Update Kalman Filter (PUKF). PUKF updates the state using
multidimensional measurements in parts. PUKF evaluates the nonlinearity of the
measurement function within a Gaussian prior by comparing the effect of the 2nd
order term on the Gaussian measurement noise. A linear transformation is
applied to measurements to minimize the nonlinearity of a part of the
measurement. The measurement update is then applied using only the part of the
measurement that has low nonlinearity and the process is then repeated for the
updated state using the remaining part of the transformed measurement until the
whole measurement has been used. PUKF does the linearizations numerically and
no analytical differentiation is required. Results show that when the
measurement geometry allows effective partitioning, the proposed algorithm
improves estimation accuracy and produces accurate covariance estimates.
