Singular value decomposition and improved frequency estimation using linear prediction

Tufts, D.W.; Kumaresan, R.

doi:10.1109/tassp.1982.1163927

Cited by 210 publications

(81 citation statements)

References 14 publications

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“…Various researchers have suggested analytical methods to estimate model order [33,[42][43][44]. Akaike [42] has suggested a final prediction error method and a cost minimization method in which a cost is assigned for extra coefficients which do not reduce model order.…”

Section: Prony's Methodsmentioning

confidence: 99%

A prony algorithm for shallow water waveguide analysis

Diemer¹

1987

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Low frequency acoustic propagation in shallow water is examined from a normal mode context. By modelling the far field pressure field as a modal sum, propagating mode characteristics of wavenumber, initial phase, attennation and amplitude may be estimated using a high resolution parameter modeling technique. The advantages of such an algorithm are the resolution of closely spaced modes in a range independent environment and the ability to analyze range dependent waveguides.This thesis presents the application of a Prony algorithm to the shallow water environment. The algorithm operates directly on the signal matrix. Synthetically generated, range independent pressure fields are used to analyze the technique'S performance and to observe its sensitivity to variations in model specifications. Noise is added to determine the threshold of acceptable performance. As a consequence of field data tests, further enhancements to the algorithm are suggested.Range dependent performance is evaluated on a coastal wedge example and geoacoustic parameter shift example.

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Section: Prony's Methodsmentioning

confidence: 99%

A prony algorithm for shallow water waveguide analysis

Diemer¹

1987

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“…Results are compared with the Koopman algorithm reported in [10] and a multisignal Prony method [20], based on the Kumaresan-Tuft approach [21] and the OMIB model in Fig. 12.…”

Section: Computational Effortmentioning

confidence: 99%

A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis

Barocio

Pal

Thornhill

et al. 2015

IEEE Trans. Power Syst.

154

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Abstract--A global multiscale method based on a dynamic mode decomposition (DMD) algorithm to characterize the global behavior of transient processes recorded using wide-area sensors is proposed. The method interprets global dynamic behavior in terms of both, spatial patterns or shapes and temporal patterns associated with dynamic modes containing essentially singlefrequency components, from which the mode shapes, frequencies and growth and decay rates of the modes can be extracted simultaneously. These modes are then used to detect the coherent and dominant structures within the data.The technique is well suited for fast wide-area monitoring and assessment of global instability in the context of modern data fusion-based estimation techniques. Results of the application of the proposed method to large, high-dimensional data sets are encouraging.

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“…Frequency estimation of multiple complex sinusoids based on the SVD is described [9], [10]. The resultant estimates are used as the initial state variables in the NMSE, which have a good accuracy when frequency of each sinusoid is time-invariant.…”

Section: Initial Estimation For the Nmsementioning

confidence: 99%

“…Fortunately, the stability (convergence) of the NMSE is also theoretically clarified [8]. Also, the singular value decomposition (SVD) [9], [10] is effectively combined with the ECKF as the initial state estimation. In this paper, the NMSE is efficiently specialized to harmonic estimation so that its computational complexity is adequately reduced.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Estimation of Harmonic Signals

Nishiyama¹

2007

IEICE Transactions on Fundamentals of Electronics, Communicatio

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Kiyoshi NISHIYAMA †a) , Member SUMMARYA nonlinear harmonic estimator (NHE) is proposed for extracting a harmonic signal and its fundamental frequency in the presence of white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a multiple sinusoidal model with staterepresentation and then efficiently specializing it for the case of harmonic estimation. The effectiveness of the NHE is verified using computer simulations.

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Singular value decomposition and improved frequency estimation using linear prediction

Cited by 210 publications

References 14 publications

A prony algorithm for shallow water waveguide analysis

A prony algorithm for shallow water waveguide analysis

A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis

Nonlinear Estimation of Harmonic Signals

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