Abstract
Two basic approaches to adaptive signal processing are in common use. The first and most direct involves substituting data-derived estimates of signal and noise autocorrelations into the standard Wiener-Hopf equation. The second uses a stochastic algorithm, such as the LMS, to minimize the mean square error directly. This paper attempts to unify these approaches by deriving an algorithm which substitutes data-derived estimates of the signal and noise autocorrelations into a recursive version of the Wiener-Hopf equation thus eliminating the need for direct matrix inversion. Although clearly an offshoot of the direct method, this algorithm, in its simplest form, is identical to the well-known LMS stochastic algorithm.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
