Showing papers on "Median filter published in 1968"
TL;DR: In this paper, eight different filtering schemes, one linear and seven nonlinear, are applied to two nonlinear scalar dynamic systems which are forced by additive noise, and simulated on a digital computer and compared on the basis of their estimation error history.
Abstract: Eight different filtering schemes, one linear and seven nonlinear, are applied to two nonlinear scalar dynamic systems which are forced by additive noise. The nonlinear measurement made of the state is corrupted by further additive noise. The various filters, derived using a continuous formulation, are simulated on a digital computer and are compared on the basis of their estimation-error history. In addition to the comparison, one system/filter combination is used to demonstrate the effects of varying the variance of the noise, of mismatched parameters, and of sampled measurements.
57 citations
TL;DR: A nonlinear filtering scheme with noiseless feedback is presented, based on a consideration of the minimum-mean-squared error filtering of independent signal samples corrupted by additive noise, and the improvement over the linear case without feedback is shown.
Abstract: A nonlinear filtering scheme with noiseless feedback is presented, based on a consideration of the minimum-mean-squared error filtering of independent signal samples corrupted by additive noise. The explicit solution for the general case is very complex. However, if the signal-to-noise ratio is assumed to be large and the nonlinear estimating filter has zero memory, the problem may be simplified by reducing it to the zero-memory prefiltering problem combined with predictive feedback. The improvement over the linear case without feedback is shown to be the product of the improvements due to the zero-memory non-linearities and the feedback. An example is considered to illustrate the improvements in the error,
1 citations