A nearest neighbors approach to multidimensional filtering

Abstract

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.