Experimental validation of a random matrix theory model for dominant mode rejection beamformer notch depth

Adaptive beamformers attempt to eliminate loud interferers in order to facilitate the detection of quiet sources. The Dominant Mode Rejection (DMR) beamformer does this by placing notches in its beampattern corresponding to signals contained in the interference subspace. This subspace is defined by...

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Bibliographic Details
Main Authors: Wage, K. E., Buck, J. R., Dzieciuch, M. A., Worcester, P. F.
Format: Conference Proceeding
Language:English
Subjects:
adaptive beamforming
Arrays
Covariance matrix
dominant mode rejection
Eigenvalues and eigenfunctions
Interference
Noise
Predictive models
random matrix theory
Vectors
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Summary:Adaptive beamformers attempt to eliminate loud interferers in order to facilitate the detection of quiet sources. The Dominant Mode Rejection (DMR) beamformer does this by placing notches in its beampattern corresponding to signals contained in the interference subspace. This subspace is defined by the eigenvectors associated with the largest eigenvalues of the sample covariance matrix. A companion paper derives an analytical model for the notch depth of the DMR beamformer using results from random matrix theory (RMT) on the statistics of the sample eigenvectors. This paper explores the validity of the DMR notch depth model using data from the 2010 Philippine Sea experiment. The measured average notch depths agree with the predictions of the RMT model.
ISSN:2373-0803
2693-3551
DOI:10.1109/SSP.2012.6319830