dc.contributor.advisor | Ho, Dominic K. C. | eng |
dc.contributor.author | Oddiraju, Swetha, 1981- | eng |
dc.date.issued | 2007 | eng |
dc.date.submitted | 2007 Summer | eng |
dc.description | The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. | eng |
dc.description | Title from title screen of research.pdf file (viewed on September 29, 2008) | eng |
dc.description | Includes bibliographical references. | eng |
dc.description | Thesis (M.S.) University of Missouri-Columbia 2007. | eng |
dc.description | Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering. | eng |
dc.description.abstract | [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Adaptive filtering is a common solution to many signal processing applications in which the environments are time varying. Its performance is limited by the misadjustment. A method to reduce the misadjustment when the additive noise in the desired signal is correlated is investigated in this work. The proposed method consists of two adaptive components: a modeling filter and a noise whitening filter. The proposed method performs better than the conventional LMS and RLS algorithms both in convergence speed and misadjustment factor. The performance of the adaptive filter when updated using LMS is observed. The improvement factor is proportional to the ratio of the noise power to white innovation power in the desired signal. The reduction in gradient noise allows larger step size, thus increasing the overall performance of the system. Both theoretical and simulation results proved that the proposed method gives a smaller misadjustment and better tracking capability than existing methods. | eng |
dc.identifier.merlin | b64879422 | eng |
dc.identifier.oclc | 259225096 | eng |
dc.identifier.uri | https://hdl.handle.net/10355/6271 | |
dc.identifier.uri | https://doi.org/10.32469/10355/6271 | eng |
dc.language | English | eng |
dc.publisher | University of Missouri--Columbia | eng |
dc.relation.ispartofcommunity | University of Missouri--Columbia. Graduate School. Theses and Dissertations | eng |
dc.rights | Access is limited to the campus of the University of Missouri--Columbia. | eng |
dc.subject | modeling filter; noise whitening filter. | eng |
dc.subject | modeling filter; noise whitening filter | eng |
dc.subject.lcsh | Adaptive filters | eng |
dc.subject.lcsh | Signal processing | eng |
dc.title | Improving performance for adaptive filtering with voice applications | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Electrical and computer engineering (MU) | eng |
thesis.degree.grantor | University of Missouri--Columbia | eng |
thesis.degree.level | Masters | eng |
thesis.degree.name | M.S. | eng |