Abstract
It is known that the efficiency of the parallel implementation of the Kalman tracking filter can be improved by reordering the elements of the state vector in a suitable manner. It is shown in the present work that the use of such a reordered state vector in place of the standard one can, by itself, result in a substantial reduction in the computational load, when the system matrices are partitioned into smaller dimension matrices, even in a uniprocessor implementation of the filter.
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