资 源 简 介
The celebrated Kalman filter, rooted in the state-space formulation or linear
dynamical systems, provides a recursive solution to the linear optimal filtering
problem. It applies to stationary as well as nonstationary environments. The
solution is recursive in that each updated estimate of the state is computed from
the previous estimate and the new input data, so only the previous estimate
requires storage. In addition to eliminating the need for storing the entire
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past observed data, the Kalman filter is computationally more efficient than
computing the estimate directly from the entire past observed data at each step
of the filtering process.
