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Numerical Computation of Wishart Eigenvalue Distributions for Multistatic Radar Detection
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Numerical Computation of Wishart Eigenvalue Distributions for Multistatic Radar Detection. |
| 개인저자 | Jones, Scott. |
| 단체저자명 | Arizona State University. Electrical Engineering. |
| 발행사항 | [S.l.]: Arizona State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 115 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781088369982 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Cochran, Douglas. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Eigenvalues of the Gram matrix formed from received data frequently appear in sufficient detection statistics for multi-channel detection with Generalized Likelihood Ratio (GLRT) and Bayesian tests. In a frequently presented model for passive radar, in which the null hypothesis is that the channels are independent and contain only complex white Gaussian noise and the alternative hypothesis is that the channels contain a common rank-one signal in the mean, the GLRT statistic is the largest eigenvalue 貫1 of the Gram matrix formed from data. This Gram matrix has a Wishart distribution. Although exact expressions for the distribution of 貫1 are known under both hypotheses, numerically calculating values of these distribution functions presents difficulties in cases where the dimension of the data vectors is large. This dissertation presents tractable methods for computing the distribution of 貫1 under both the null and alternative hypotheses through a technique of expanding known expressions for the distribution of 貫1 as inner products of orthogonal polynomials. These newly presented expressions for the distribution allow for computation of detection thresholds and receiver operating characteristic curves to arbitrary precision in floating point arithmetic. This represents a significant advancement over the state of the art in a problem that could previously only be addressed by Monte Carlo methods. |
| 일반주제명 | Electrical engineering. Statistics. Applied mathematics. |
| 언어 | 영어 |
| 바로가기 | 
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