Comparison of linear distributed-parameter filters to lumped approximants



Optimal distributed-parameter filters are commonly implemented using approximating lumped Kalman filtering theory. The effect of such an approximation is investigated. A theoretical development shows that there is a loss in the spatial noise correlation for the lumped approximants. Two numerical examples of engineering significance illustrate that one result of this loss is slower filter convergence for the lumped approximants relative to the full distributed-parameter filters.