Online Adaptive Estimation of Sparse Signals: Where RLS Meets the $\ell_1$-Norm

Angelosante, Daniele; Bazerque, Juan Andrés; Giannakis, Georgios B.

doi:10.1109/tsp.2010.2046897

Cited by 241 publications

(239 citation statements)

References 24 publications

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“…A possible approach is offered by On-line coordinate descent iterative minimizers (Angelosante, Bazerque and Georgios, 2010). The algorithm is modified here to develop an online solver of (7) and compute a closed-form solution per iteration.…”

Section: Online Methods Based On An Online (Cyclic) Coordinate Descentmentioning

confidence: 99%

An On-line Method for Estimation of Piecewise Constant Parameters in Linear Regression Models

Salehpour

Gustafsson

Johansson

2011

IFAC Proceedings Volumes

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We present an on-line method for detecting changes and estimating parameters in AR(X) models. The method is based on the assumption of piecewise constant parameters resulting in a sparse structure of their derivative. To illustrate the algorithm and its performance, we apply it to the change in the model parameters of some ARX models. The example illustrates that the new method shows good performance for an AR(X) model with abrupt changes in the parameters.

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Section: Online Methods Based On An Online (Cyclic) Coordinate Descentmentioning

confidence: 99%

An On-line Method for Estimation of Piecewise Constant Parameters in Linear Regression Models

Salehpour

Gustafsson

Johansson

2011

IFAC Proceedings Volumes

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“…One attractive approach to solve such an optimization problem is to run an online CCD algorithm due to its speed and numerical stability [30,31]. The CCD algorithm separately minimizes the cost function (22) for each entry of β and can admit a closed-form solution.…”

Section: Recursive Penalized Wavelet Estimator For Online Plbm Identimentioning

confidence: 99%

“…However, most existing penalized wavelet estimators of the partially linear model are processed in batch form using iterative algorithms [14,23,27], which are not suitable for online implementation purposes. Recent advances in sparse linear model identification have shown that the cyclic coordinate descent (CCD) algorithm provides an efficient means of solving the penalized least squares (LS) problem [28,29] and can be implemented online in a recursive fashion [30,31]. This feature motivates us to extend this algorithm to the wavelet-based partially linear model and develop a recursive penalized wavelet estimator based on a modified online CCD algorithm.…”

Section: Introductionmentioning

confidence: 99%

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Jiang

Zhang

2013

Energies

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Abstract:Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

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“…For many applications, such as, for instance, audio processing, data is often generated online, with large correlation between consecutive frames, and with a varying degree of non-stationarity. To better accommodate these conditions, one may use a sparse recursive least squares (RLS) approach (see, e.g., [16,17]), such as the one derived in [18] for the multi-pitch estimation problem. In a recent effort, the sparse iterative covariance-based estimator (SPICE) [19] utilizes a criteria for covariance fitting, originally developed within array processing, to form sparse estimates without the need of selecting hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

Online group-sparse estimation using the covariance fitting criterion

Kronvall

Adalbjörnsson

Nadig

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved by reformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.

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Online Adaptive Estimation of Sparse Signals: Where RLS Meets the $\ell_1$-Norm

Cited by 241 publications

References 24 publications

An On-line Method for Estimation of Piecewise Constant Parameters in Linear Regression Models

An On-line Method for Estimation of Piecewise Constant Parameters in Linear Regression Models

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Online group-sparse estimation using the covariance fitting criterion

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