Statistical Performance Analysis of DOA Estimation Methods, Ph.D. Dissertation Sharif University of Technology ; Nayebi, Mohammad Mahdi (Supervisor) ; Aref, Mohammad Reza (Supervisor)
Abstract
In this thesis, we investigate the statistical performance of the array processing algorithms. Theoretical performance analysis leads to better methods that outperform the existing algorithms. Besides, analytical performance analysis, results in a more profound understanding of the nature of the considered problem. First of all, problem of covariance matrix estimation, in the non-Gaussian signal case will be investigated. We will focus on a nonparametric estimator which relies on the sign of the data to estimate the covariance matrix on an element-by-element basis. It was known that, sign estimator may give invalid covariance estimates in higher dimensions of the data. We prove this fact in...
Cataloging briefStatistical Performance Analysis of DOA Estimation Methods, Ph.D. Dissertation Sharif University of Technology ; Nayebi, Mohammad Mahdi (Supervisor) ; Aref, Mohammad Reza (Supervisor)
Abstract
In this thesis, we investigate the statistical performance of the array processing algorithms. Theoretical performance analysis leads to better methods that outperform the existing algorithms. Besides, analytical performance analysis, results in a more profound understanding of the nature of the considered problem. First of all, problem of covariance matrix estimation, in the non-Gaussian signal case will be investigated. We will focus on a nonparametric estimator which relies on the sign of the data to estimate the covariance matrix on an element-by-element basis. It was known that, sign estimator may give invalid covariance estimates in higher dimensions of the data. We prove this fact in...
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