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Donald W. Tufts
Researcher at University of Rhode Island
Publications - 105
Citations - 5823
Donald W. Tufts is an academic researcher from University of Rhode Island. The author has contributed to research in topics: Estimation theory & Linear prediction. The author has an hindex of 31, co-authored 105 publications receiving 5681 citations. Previous affiliations of Donald W. Tufts include New Jersey Institute of Technology.
Papers
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Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihood
Donald W. Tufts,Ramdas Kumaresan +1 more
TL;DR: In this paper, the frequency estimation performance of the forward-backward linear prediction (FBLP) method was improved for short data records and low signal-to-noise ratio (SNR) by using information about the rank M of the signal correlation matrix.
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Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise
Ramdas Kumaresan,Donald W. Tufts +1 more
TL;DR: In this paper, the estimation procedure presented here makes use of "backward prediction" in addition to singular value decomposition (SVD) for accurate estimation of closely spaced frequencies of sinusoidal signals in noise.
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Estimating the Angles of Arrival of Multiple Plane Waves
Ramdas Kumaresan,Donald W. Tufts +1 more
TL;DR: In this article, a polynomial D(z) with special properties is constructed from the eigenvectors of C, the zeros of which give estimates of the angle of arrival.
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Adaptive detection using low rank approximation to a data matrix
I.P. Kirsteins,Donald W. Tufts +1 more
TL;DR: Using an accurate formula for the error in approximating a low rank component, the performance of adaptive detection based on reduced-rank nulling is calculated and a generalized likelihood-ratio test (GLRT) is presented for adaptively detecting a lowRank signal in the presence of low rank interference.
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Singular value decomposition and improved frequency estimation using linear prediction
Donald W. Tufts,Ramdas Kumaresan +1 more
TL;DR: LP estimation of frequencies can be greatly improved at low SNR by singular value decomposition (SVD) of the LP data matrix, as is done in Pisarenko's method and its variants.