Information Formulation of the UDU Kalman Filter.

Abstract

A new information formulation of the Kalman filter is presented where the information matrix is parameterized as the product of an upper triangular matrix, a diagonal matrix, and the transpose of the triangular matrix (UDU factorization). The UDU factorization of the Kalman filter is known for its numerical stability, this work extends the technique to the information filter. A distinct characteristic of the new algorithm is that measurements can be processed as vectors, while the classic UDU factorization requires scalar measurement processing, i.e. a diagonal measurement noise covariance matrix.