Output prediction under scarce data operation: control applications

Albertos, Pedro; Sanchis, Roberto; Sala, Antonio

doi:10.1016/s0005-1098(99)00078-3

Cited by 52 publications

(38 citation statements)

References 9 publications

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“…Makhoul et al [14], [15] proposed an adaptive estimation of k n . An unnormalized RLSL algorithm is thus obtained by combining the adaptive estimation on time of k (l) n and the recursive equations on order (1). The recursive equations, at time n, of the RLSL algorithm based on the reflection coefficient calculated in [14] are:…”

Section: B Rlsl Algorithmmentioning

confidence: 99%

“…Several methods, such as in [1], [2], [8], [12], [16], [18], have already been developed for on line processing of AR signals with missing data. They use at each time only the past available data.…”

Section: Introductionmentioning

confidence: 99%

“…The predictor is adaptive, however, it is not recursive. In [1], the problem of recursive estimation of the output in missing-data situations is addressed. In [8], Isaksson derives a recursive EM algorithm for the identification in missing data situations.…”

Section: Introductionmentioning

confidence: 99%

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Lattice Algorithm for Adaptive Stable Identification and Robust Reconstruction of Nonstationary AR Processes With Missing Observations

Zgheib¹,

Fleury²,

Lahalle³

2008

IEEE Trans. Signal Process.

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To cite this version:Rawad Zgheib, Gilles Fleury, Elisabeth Lahalle. Lattice algorithm for adaptive stable identification and robust reconstruction of non stationary AR processes with missing observations. IEEE Abstract-This paper deals with the problem of adaptive reconstruction and identification of non stationary AR processes with randomly missing observations. Existent methods use a direct realization of the filter. Therefore, the estimated parameters may not correspond to a stable all-pole filter. In addition, when the probability of missing a sample is high, existent methods may converge slowly or even fail to converge. We propose, at our knowledge, the first algorithm based on the lattice structure for online processing of signals with missing samples. It is an extension of the RLSL algorithm to the case of missing observations, using a Kalman filter for the prediction of missing samples. The estimated parameters guarantee the stability of the corresponding all-pole filter. In addition it is robust to high probabilities of missing a sample. It offers a fast parameter tracking even for high probabilities of missing a sample. It is compared to the Kalman pseudo linear RLS algorithm, an already proposed algorithm using a direct realization of the filter. The proposed algorithm shows better performance in reconstruction of audio signals.

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Section: B Rlsl Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lattice Algorithm for Adaptive Stable Identification and Robust Reconstruction of Nonstationary AR Processes With Missing Observations

Zgheib¹,

Fleury²,

Lahalle³

2008

IEEE Trans. Signal Process.

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“…• On line adaptive methods such as in [6,7,8,9,10]. In [8], an LMS-like algorithm for simultaneous reconstruction and identification is developed.…”

Section: Introductionmentioning

confidence: 99%

New Fast Algorithm for Simultaneous Identification and Optimal Reconstruction of Non Stationary AR Processes with Missing Observations

Zgheib

Fleury

Lahalle

2006

2006 IEEE 12th Digital Signal Processing Workshop &Amp;amp; 4th IEEE Signal Processing Education Workshop

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This paper deals with the problem of adaptive reconstruction and identification of AR processes with randomly missing observations. A new real time algorithm is proposed. It uses combined pseudo-linear RLS algorithm and Kalman filter. It offers an unbiased estimation of the AR parameters and an optimal reconstruction error in the least mean square sense. In addition, thanks to the pseudo-linear RLS identification, this algorithm can be used for the identification of non stationary AR signals. Moreover, simplifications of the algorithm reduces the calculation time, thus this algorithm can be used in real time applications.

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“…Además, se consideran también en esta área los sistemas de control con pérdida de información que tienen una relación directa con la limitación en ancho de banda disponible en el enlace compartido. La línea de investigación dedicada al control multifrecuencia y con muestreo no convencional incluye gran cantidad de trabajos publicados entre los que cabe citar como ejemplos de especial interés para el trabajo desarrollado las siguientes referencias: [Skl55], [Kra57], [Kal59], [Jur67], [Mey75], [Gla83], [Ara86], [Tho86], [Alb90], [God90], [Mey90], [Ara93], [Sal92], [Sal93], [Lon94], [Alb96], [Cam99], [Alb99a], [Alb99c], [Tan99], [Tor99], [Yup99], [Sal00b].…”

Section: 2-referencias Relacionadas Con Los Scbrunclassified

Sistemas de control basados en red. Modelado y diseño de estructuras de control.

Calvo¹,

Fermín²

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Output prediction under scarce data operation: control applications

Cited by 52 publications

References 9 publications

Lattice Algorithm for Adaptive Stable Identification and Robust Reconstruction of Nonstationary AR Processes With Missing Observations

Lattice Algorithm for Adaptive Stable Identification and Robust Reconstruction of Nonstationary AR Processes With Missing Observations

New Fast Algorithm for Simultaneous Identification and Optimal Reconstruction of Non Stationary AR Processes with Missing Observations

Sistemas de control basados en red. Modelado y diseño de estructuras de control.

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