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Analysis of RMT Model for Dominant Mode Rejection Beamformer notch depth in PhilSea10

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dc.contributor.advisor Wage, Kathleen E Kamaraju, Sai Kasyap
dc.creator Kamaraju, Sai Kasyap 2016-12-08 2017-12-07T21:22:30Z 2017-12-07T21:22:30Z
dc.identifier doi:10.13021/G8ZH5C
dc.description.abstract In sonar array signal processing, one of the essential objectives is mitigating the effect of loud non-stationary interfering sources. The Dominant Mode Rejection adaptive beam- former is a beamformer that is often used to detect low powered signals in the presence of high power interferers. The DMR beamformer constructs its weights using a structured co- variance estimate which is obtained by doing the principal component analysis of the sample covariance matrix (SCM). Notch depth is a parameter which quantifies how well a beam- former can attenuate an interferer. In prior work, Buck and Wage developed an analytical model that predicts average DMR notch depth as a function of interferer strength and the number of snapshots available to estimate the eigendecomposition of the SCM [IEEE Stat. Sig. Proc. Workshop, 2012]. The DMR notch depth model is based on results from random matrix theory on the accuracy of the eigenvectors of the SCM. This thesis assesses the validity of the notch depth model using data recorded with a vertical line array during the 2010 Philippine Sea Experiment. In the PhilSea10 analysis the DMR beamformer removes loud mechanical vibration noise (array strum) from the data set. Results show good agreement with theory when the experimental data satisfies the assumptions of the model.
dc.language.iso en en_US
dc.subject random matrix theory en_US
dc.subject notch depth en_US
dc.subject array signal processing en_US
dc.subject DMR Beamformer en_US
dc.subject PhilSea10 en_US
dc.subject beamforming en_US
dc.title Analysis of RMT Model for Dominant Mode Rejection Beamformer notch depth in PhilSea10 en_US
dc.type Thesis en_US Master of Science in Electrical Engineering en_US Master's en_US Electrical Engineering en_US George Mason University en_US

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