Efficient parallel implementation of target tracking Kalman filter

A parallel algorithm for solving an n-state Kalman filter on an (n+1)-cell linear array is described. The approach is to update the columns of the filter covariance matrix in parallel to balance the computations and minimize the communications. The algorithm is the basis for the mapping of an extended Kalman filter on the Warp computer. The Warp implementation is written in a high-level language and achieves a measured speedup of almost 300 over the same filter running on a Sun workstation. Efficient algorithm mapping is the key to achieve the high-speed filter performance.<
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