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588
URV ESPRIT for Tracking timeVarying Signals
, 1994
"... Abstruct ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at an array of sensors. Unfortunately, its computational burden makes it unsuitable for real time processing of signals with timevarying directions of arrival. In this work we develop a n ..."
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Cited by 14 (2 self)
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constant and timevarying signals. We find that the URVbased ESPRIT algorithm is effective for estimating timevarying directionsofarrival at considerable computational savings over the SVDbased algorithm. I.
An Adaptive ESPRIT Based on URV Decomposition*
"... ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at am array of sensors. Unfortunately, its computational burden & it unsuitable for real time processing of signals with timevarying directions of arrival. In this work we develop a new impleme ..."
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ESPRIT is an algorithm for determining the fixed directions of arrival of a set of narrowband signals at am array of sensors. Unfortunately, its computational burden & it unsuitable for real time processing of signals with timevarying directions of arrival. In this work we develop a new
Tracking TimeVarying CoefficientFunctions
"... A conditional parametric ARXmodel is an ARXmodel in which the parameters are replaced by smooth functions of an, possibly multivariate, external input signal. These functions are called coefficientfunctions. A method, which estimates these functions adaptively and recursively, and hence allows for ..."
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Cited by 5 (3 self)
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for online tracking of the coefficientfunctions is suggested. Essentially, in its most simple form, this method is a combination of recursive least squares with exponential forgetting and local polynomial regression. However, it is argued, that it is appropriate to let the forgetting factor vary
Tracking Timevarying Graphical Structure
"... Abstract Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In realworld environments, however, such changes oft ..."
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often occur without warning or signal. Realworld data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, realtime manner. We
Tracking control of nonlinear systems using sliding surfaces with application to robot manipulators
 International Journal of Control
, 1983
"... We develop a methodology of feedback control to achieve accurate tracking in a class of nonlinear, timevarying systems in the presence of disturbances and parameter variations. The methodology uses in its idealized form piecewise continuous feedback control, resulting in the state trajectory &apos ..."
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Cited by 157 (1 self)
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We develop a methodology of feedback control to achieve accurate tracking in a class of nonlinear, timevarying systems in the presence of disturbances and parameter variations. The methodology uses in its idealized form piecewise continuous feedback control, resulting in the state trajectory
Parameter Estimation And Tracking For TimeVarying Sinusoids
, 2002
"... Parametric modeling permits an efficient representation of audio signals and is increasingly utilized for very low bit rate coding applications. Such systems are based on a decomposition of the audio signal into components that are described by appropriate source models and represented by model para ..."
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Cited by 4 (0 self)
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parameters. Commonly used components types are sinusoidal trajectories, harmonic tones, transients, and noise. Proper estimation and tracking of sinusoids in case of vibrato or portamento is vital, yet difficult due to the uncertainty principle for timefrequency resolution. This paper presents a reliable
Generalized URV Subspace Tracking LMS Algorithm 1
"... The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input autocorrelation matrix is illconditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks the subspac ..."
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The convergence rate of the Least Mean Squares (LMS) algorithm is poor whenever the adaptive lter input autocorrelation matrix is illconditioned. In this paper we propose a new LMS algorithm to alleviate this problem. It uses a data dependent signal transformation. The algorithm tracks
Compression Schemes for TimeVarying Sparse Signals
"... Abstract—In this paper, we will investigate an adaptive compression scheme for tracking timevarying sparse signals with possibly varying sparsity patterns and/or order. In particular, we will focus on sparse sensing, which enables a completely distributed compression and simplifies the sampling ar ..."
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Cited by 1 (1 self)
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Abstract—In this paper, we will investigate an adaptive compression scheme for tracking timevarying sparse signals with possibly varying sparsity patterns and/or order. In particular, we will focus on sparse sensing, which enables a completely distributed compression and simplifies the sampling
Optimal Particle Filters for Tracking a TimeVarying Harmonic or Chirp Signal
"... Abstract—We consider the problem of tracking the timevarying (TV) parameters of a harmonic or chirp signal using particle filtering (PF) tools. Similar to previous PF approaches to TV spectral analysis, we assume that the model parameters (complex amplitude, frequency, and frequency rate in the chi ..."
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Cited by 2 (2 self)
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Abstract—We consider the problem of tracking the timevarying (TV) parameters of a harmonic or chirp signal using particle filtering (PF) tools. Similar to previous PF approaches to TV spectral analysis, we assume that the model parameters (complex amplitude, frequency, and frequency rate
An Updating Algorithm for Subspace Tracking
 IEEE Trans. Signal Processing
, 1992
"... In certain signal processing applications it is required to compute the null space of a matrix whose rows are samples of a signal with p components. The usual tool for doing this is the singular value decomposition. However, the singular value decomposition has the drawback that it requires O(p 3 ..."
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Cited by 114 (14 self)
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) operations to recompute when a new sample arrives. In this paper, we show that a different decomposition, called the URV, decomposition is equally effective in exhibiting the null space and can be updated in O(p 2 ) time. The updating technique can be run on a linear array of p processors in O(p) time. 1
Results 1  10
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