Showing papers on "Median filter published in 1971"
TL;DR: In this article, an algorithm is derived for multichannel time series data processing, which maintains specified initial multiple filter constraints for known signal or noise sources while simultaneously adapting the filter to minimize the effect of the unknown noise field.
Abstract: An algorithm is derived for multichannel time‐series data processing, which maintains specified initial multiple filter constraints for known signal or noise sources while simultaneously adapting the filter to minimize the effect of the unknown noise field. Problems of implementing the technique such as convergence, determination of a starting filter, and comparison of results with conventional filters are discussed and illustrated with data from a vertical seismic array. The procedure is shown to be stable and obtains approximately 3–4 db gain in S/N improvement over conventional Wiener filtering in the band 1 to 3 hz.
40 citations
TL;DR: In this article, the mean square errors of Pf and Ps were compared with the linear least squares filtered and smoothed estimates of a stationary process of spectral density S(ω) in white noise with spectral density No.
Abstract: Comparison is made between the mean-square errors Pf and Ps associated with the linear least-squares filtered and smoothed estimates of a stationary process of spectral density S(ω) in white noise of spectral density No. A universal curve is obtained which relates the minimum possible value of Ps/Pf to ωmax{S(ω) No}. The curve sets a bound on the maximum improvement over filtering which smoothing will offer, in terms of the maximum signal/noise ratio.
18 citations
TL;DR: In this paper, the multidimensional linear filtering problem for a signal in white noise is considered, and formulas are given for optimum causal and non-causal filters and the associated errors.
Abstract: The multidimensional linear-filtering problem for a signal in white noise is considered, and formulas are given for optimum causal and noncausal filters and the associated errors.
5 citations
TL;DR: In this paper, the effect of errors due to incorrect colored noise on optimal filtering algorithms has been investigated and the matrix differential equations have been derived for the computation of the actual covariance.
Abstract: The effect of errors due to incorrect colored noise on optimal filtering algorithms has been investigated. The matrix differential equations have been derived for the computation of the actual covariance.
1 citations