Nonlocal Methods for Signal Detection and Estimation in the Dependent Nonstationary Environment
Abstract
We have obtained a number of results pertaining to image compression, robust estimation, and robust signal detection. All of this work has admitted the presence of data whose statistics are imperfectly known. Our results have featured adaptivity, flexibility, and nontraditional approaches. In order to employ more realistic statistical models, we have directed our research to admit nonstationarity and dependency. Much of our work in robust estimation and detection has employed a geometric approach which we have originated in past research. Our geometric techniques provide a quantitive way to measure the degree of robustness, thus offering the designer more flexibility in meeting the performance/robustness needs of the user. Our results include generalized robustness criteria involving curvature as well as manifold slope, as well as generalized nonlocal robustness criteria which supersede prior nonlocal criteria based on the worst case perspective. In addition, we have applied the geometric perspective to show how linear estimation algorithms can be modified to optimize a weighted combination of performance and robustness, thus offering the user the option of selecting various performance/robustness combinations as deemed appropriate for a specific application.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 30, 1991
- Accession Number
- ADA241645
Entities
People
- Don R. Halverson
Organizations
- Texas A&M University