Optimum differentiation using Kalman filter theory

Consideration is given to the construction of an optimum differentiator to give the minimum-variance unbiased estimate of the first derivatives of random signals corrupted by white noise. It is assumed that the signals are differentiable and are the outputs of a known linear finite-dimensional (possibly time-varying) system excited by white noise. Extension of the results to consider higher-order differentiation is straightforward.