Why can Wiener-Hopf reach accurate solutions with poor statistical estimates?

Stephan Weiss; Ian K. Proudler

doi:10.1109/SSP64130.2025.11073196

Why can Wiener-Hopf reach accurate solutions with poor statistical estimates?

Stephan Weiss, Ian K. Proudler

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

6 Downloads (Pure)

Abstract

The Wiener-Hopf solution is based on estimates of the covariance matrix and the cross-correlation vector, which will likely be poor for small sample sizes. Yet the solution can be very accurate. We explore why this is, and why it is disadvantageous to separately estimate the two statistics, or try to include explicit knowledge of the input process to the adaptive system. The results are based on a least squares interpretation of Wiener-Hopf and an exploration of the variances of estimates, which we underpin by simulations.

Original language	English
Title of host publication	2025 IEEE Statistical Signal Processing Workshop (SSP)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	146-150
Number of pages	5
ISBN (Electronic)	9798331518004
ISBN (Print)	9798331518011
DOIs	https://doi.org/10.1109/SSP64130.2025.11073196
Publication status	Published - 16 Jul 2025
Event	23rd IEEE Statistical Signal Processing Workshop - Edinburgh, United Kingdom Duration: 8 Jun 2025 → 11 Jun 2025 https://2025.ieeessp.org/

Publication series

Name	IEEE/SP Workshop on Statistical Signal Processing (SSP)
ISSN (Print)	2373-0803
ISSN (Electronic)	2693-3551

Conference

Conference	23rd IEEE Statistical Signal Processing Workshop
Abbreviated title	SSP 2025
Country/Territory	United Kingdom
City	Edinburgh
Period	8/06/25 → 11/06/25
Internet address	https://2025.ieeessp.org/

Keywords

Wiener-Hopf solution
statistical signal processing
linear filtering
covariance matrix
cross-correlation vector

Access to Document

10.1109/SSP64130.2025.11073196

Weiss-Proudler-IEEE-SSP-2025-Why-can-Wiener-Hopf-reach-accurate-solutions-with-poor-statistical-estimatesAccepted author manuscript, 814 KBLicence: CC BY 4.0

Cite this

@inproceedings{9979609822a2430c90df758966f509ec,

title = "Why can Wiener-Hopf reach accurate solutions with poor statistical estimates?",

abstract = "The Wiener-Hopf solution is based on estimates of the covariance matrix and the cross-correlation vector, which will likely be poor for small sample sizes. Yet the solution can be very accurate. We explore why this is, and why it is disadvantageous to separately estimate the two statistics, or try to include explicit knowledge of the input process to the adaptive system. The results are based on a least squares interpretation of Wiener-Hopf and an exploration of the variances of estimates, which we underpin by simulations.",

keywords = "Wiener-Hopf solution, statistical signal processing, linear filtering, covariance matrix, cross-correlation vector",

author = "Stephan Weiss and Proudler, \{Ian K.\}",

year = "2025",

month = jul,

day = "16",

doi = "10.1109/SSP64130.2025.11073196",

language = "English",

isbn = "9798331518011",

series = "IEEE/SP Workshop on Statistical Signal Processing (SSP)",

publisher = "IEEE",

pages = "146--150",

booktitle = "2025 IEEE Statistical Signal Processing Workshop (SSP)",

note = "23rd IEEE Statistical Signal Processing Workshop, SSP 2025 ; Conference date: 08-06-2025 Through 11-06-2025",