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In this correspondence, we propose an efficient estimator of optimal memory (averaging interval) for discrete-time finite impulse response (FIR) filters in state-space. Its crucial property is that only real measurements and the filter output are involved with no reference and noise statistics. Testing by the two-state polynomial model has shown a very good correspondence with predicted values. Even...
We address two efficient estimators of optimal memory for FIR filters in discrete-time state-space via the conditional mean square error and real measurement. In the latter case, the algorithm does not involve neither a reference nor the noise covariances, but requires a learning circle. Although a justification has been provided for the Kalman-like unbiased FIR filter, the estimators can be used...
An extended unbiased finite impulse response (EFIR) filtering algorithm is examined for nonlinear discrete-time state-space models corrupted by additive white Gaussian noise. The algorithm is represented in the Kalman-like form ignoring noise statistics and initial errors, provided an averaging interval of N points. The first-order (EFIR1) and second-order (EFIR2) filters are compared to the relevant...
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