Information Formulation of the UDU Kalman Filter

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 a triangular matrix (referred as UDU factorization). The UDU factorization of the Kalman filter is known for its numerical stability; this paper 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.