A dimensionality reducing model for distributed filtering
E. S. Angel, Adit Jain · IEEE Transactions on Automatic Control · 1973
The necessity of filtering noisy data generated by multidimensional processes arises in many diverse settings. The direct application of the Kalman-Bucy results is hindered by dimensionality difficulties inherent in multidimensional problems. This paper shows that for linear steady-state problems significant dimensionality reductions can be accomplished, thus making routine the solution of many interesting problems.