A computationally efficient estimation technique is presented for a class of
nonlinear systems consisting of memoryless nonlinearities combined with linear
dynamic processes. The modeling approach is based on a useful sampled-data
method for simulating such systems by adding a system state for each nonlinear
element. The nonlinear estimator is next developed along the lines of the Kalman
filter, but in contrast to the Extended Kalman Filter (EKF) the present approach
does not require the linearization step after each recursive cycle. In addition, it
also appears free from the well known divergence problems associated with the
EKF. It is demonstrated that this new method is directly applicable to those
feedback systems with both major nonlinearities, for example saturating gain
blocks, and stochastic disturbances-- an example extremely difficult to handle with
EKF techniques.
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