Abstract

There are many different methods of filtering that have been known to provide better results than basic Sample-Matrix-Inversion (SMI). Providing a well-conditioned covariance matrix to these filters will provide a higher Signal to Interference plus Noise Ratio (SINR) than SMI filtering when there are large numbers of receiver element and interference signals. The available number of coherent snapshots that will be used for filter estimation is “not large enough” with respect to the array dimensionality. Common techniques to deal with this situation is to use Krylov or Singular Value Decomposition (SVD) reduced rank filtering. Auxiliary Vector (AV) filtering with the CV-MOV and J Divergence methods of choosing a termination index is improved upon by the hybrid estimation technique JMOV. When the Signal to Noise Ratio (SNR) is below 0 [dB] the signal of interest is buried in noise and estimation of the total number of signals present in a system is poor. If we fix the eigenvalues to be constant for eigenvectors that are not thought to correspond to signals, we use the eigenvector information that we have already went through the effort of performing SVD for. Sometimes the Eigenvalue Fixing (EIF) filtering produces better results than reduced rank filtering alone. The performance of the aforementioned filters when utilizing modern covariance matrix estimates is analyzed by varying parameters of the system model.

Library of Congress Subject Headings

Signal processing--Mathematics; Mathematical optimization; Electronic noise

Publication Date

12-2018

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Panos P. Markopoulos

Advisor/Committee Member

Sohail A. Dianat

Advisor/Committee Member

Andres Kwasinski

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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