A DISTRIBUTED AND ITERATIVE METHOD FOR SQUARE-ROOT FILTERING IN SPACE-TIME ESTIMATION

We describe a distributed and iterative approach to perform the unitar y transformations in the square root information filter implementation of the Kalman filter, providing an alternative to the common QR facto rization-based approaches. The new approach is useful in approximate c omputation of filtered estimates for temporally evolving random fields defined by local interactions and observations. Using several example s motivated by computer vision applications, we demonstrate that near- optimal estimates can be computed for problems of practical importance using only a small number of iterations, which can be performed in a finely parallel manner over the spatial domain of the random field.