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M

Martin Morf

Researcher at Stanford University

Publications -  110
Citations -  5630

Martin Morf is an academic researcher from Stanford University. The author has contributed to research in topics: Matrix (mathematics) & Covariance. The author has an hindex of 32, co-authored 110 publications receiving 5565 citations.

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Displacement ranks of matrices and linear equations

TL;DR: In this paper, the concept of displacement ranks is introduced to measure how close a given matrix is to Toeplitz matrices, and it is shown that these non-Toeplitzer matrices should be invertible with a complexity between O(N2 and O(3).
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New results in 2-D systems theory, part II: 2-D state-space models—Realization and the notions of controllability, observability, and minimality

TL;DR: In this article, a comparison between the different state-space models is presented and proper definitions of state, controllability and observability and their relations to minimality of 2D systems are discussed.
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Recursive least squares ladder estimation algorithms

TL;DR: A Hilbert space approach to the derivations of magnitude normalized signal and gain recursions is presented and normalized forms are expected to have even better numerical properties than the unnormalized versions.
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Fast calculation of gain matrices for recursive estimation schemes

TL;DR: In this paper, the authors presented a method of calculating these vectors with proportional-to-Np operations and memory locations, in contrast to the conventional way which requires proportional-top-N 2 operations and Np memory locations.
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Inverses of Toeplitz Operators, Innovations, and Orthogonal Polynomials

TL;DR: In this article, the authors describe several interconnections between the topics mentioned in the title and show how some previously known formulas for inverting Toeplitz operators in both discrete and contirected setting can be used to obtain the same result.