M. Dentino, etc, "Adaptive Filtering in the Frequency Domain", Proc. of IEEE, vol. 66, pp. 1658-1659, Dec. 1978. Home/Search Document Not in Database Summary Related Articles Check |
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A Kepstrum approach to Filtering, Smoothing and Prediction. - Moir, Barrett (2002) (Correct)
....which a proceduremust becarried out to enable the filter to be causal. The finalanswer is complicated and too dependent on poorly known parameters. The literature has many approaches to adaptive minimummean squared error filteringandperhapsthemost populararebasedon the adaptive noise cancellation [14,15,16]whichuses multiplesensorsand a least mean squares (LMS) weight estimation scheme to arrive atan adaptive finite impulse response (FIR) filter. Self tuning filters,smoothers and predictors have also been tried which are similar butbased instead on an adaptive infiniteimpulse response (IIR)methods ....
M Dentino, J McCool & B Widrow, (1978)Adaptive filtering in the frequency domain. Proc.IEEE, vol.66, no.12, pp1658-1659
Two-Layer Linear Structures For Fast Adaptive Filtering - Beaufays (1995) (1 citation) Self-citation (Widrow) (Correct)
....i.e. the transpose conjugate [23] The properties of complex LMS are very similar to those of real LMS, although slight differences can be observed in terms of mean square convergence and stability performance [24] 2.2. 4 The Block LMS Algorithm Block LMS is a partially batched extension of LMS [12, 10]. The instantaneous error gradient, c r , is computed at each iteration as in regular non block LMS, but rather than being used right away to update the weights, it is buffered for a certain number, L, of iterations. The weight update takes place every L iterations and is made proportional to ....
....from the fact that the block gradient in Eq. 2.34 can be seen as a linear correlation between the input signal and the output error signal. It can therefore be implemented efficiently by taking the Fourier transforms of the two signals, computing their product, and inverse transforming the result [12, 10]. The computational efficiency of this method counterbalances the slowliness of the weight convergence. We will see in section 2.4 that a whole class of transform domain algorithms is based on this principle. 2.3 The RLS Algorithm The Recursive Least Squares (RLS) algorithm implements recursively ....
M. Dentino, J. McCool, and B. Widrow. Adaptive filtering in the frequency domain. Proc. of the IEEE, 66(12):1658--1659, December 1978.
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M. Dentino, etc, "Adaptive Filtering in the Frequency Domain", Proc. of IEEE, vol. 66, pp. 1658-1659, Dec. 1978.
Application of Antenna Arrays to Mobile Communications, Part II.. - Godara (1997) (66 citations) (Correct)
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M. Dentino, J. McCool, and B. Widrow, "Adaptive filtering in frequency domain," Proc. IEEE, vol. 66, pp. 1658--1659, 1978.
Estimation-Based Adaptive Filtering and Control - Sayyar-Rodsari (1999) (Correct)
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M. Dentino, J. McCool, and B. Widrow. Adaptive Filtering in Frequency Domain. Proceedings of the IEEE, 66:1658--1659, December 1978.
Estimation-Based Synthesis of H∞-Optimal.. - Sayyarrodsari.. (2001) (Correct)
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B. W. M. Dentino and J. McCool, "Adaptive filtering in frequency domain, " Proc. IEEE, vol. 66, pp. 1658--1659, Dec. 1978.
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