Improved multiband structured subband adaptive filter algorithm with L0-norm regularization for sparse system identification

Heydari, Esmail; Abadi, Mohammad Shams Esfand; Khademiyan, Seyed Mahmoud

doi:10.1016/j.dsp.2021.103348

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Remark 2: The proposed AOP-SA-RNSAF update generalizes different algorithms, depending on the choice of ϕ(e) in (4b) and f (w) in (3). In the literature, several robust criteria against impulsive noises [6], [7], [9], [10], [14] defined by ϕ(e) and sparsity-aware penalties [15], [16], [18], [19], [22] defined by f (w) have been studied, which can be applied in the AOP-SA-RNSAF. Nevertheless, this paper does not consider the effect of different choices of ϕ(e) and/or f (w), which is worth studying in future work.…”

Section: B Adaptation Of the Sparsity Penalty Weightmentioning

confidence: 99%

“…Later, robust PNSAF algorithms were also presented [10], [14] to deal with impulsive noises. On the other hand, the family of sparsityaware algorithms incorporates the sparsity-aware penalty into the original NSAF's and PNSAF's cost functions; as a result, sparsity-aware NSAF (SA-NSAF) [15], [16] and sparsityaware PNSAF [17] algorithms were developed. In sparse system identification, these sparsity-aware algorithms can obtain better convergence and steady-state performance than their original counterparts.…”

Section: Introductionmentioning

confidence: 99%

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Sparsity-Aware Robust Normalized Subband Adaptive Filtering Algorithms With Alternating Optimization of Parameters

Huang

et al. 2022

IEEE Trans. Circuits Syst. II

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This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty. Furthermore, by alternating optimization of the parameters (AOP) of the algorithm, including the step-size and the sparsity penalty weight, we develop the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also obtains low steadystate misadjustment for sparse systems. Simulations in various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm outperforms existing techniques.

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Section: B Adaptation Of the Sparsity Penalty Weightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparsity-Aware Robust Normalized Subband Adaptive Filtering Algorithms With Alternating Optimization of Parameters

Huang

et al. 2022

IEEE Trans. Circuits Syst. II

View full text Add to dashboard Cite

show abstract

“…Remark 2: The proposed AOP-SA-RNSAF update generalizes different algorithms, depending on the choice of ϕ(e) in (4b) and f (w) in ( 3). In the literature, several robust criteria against impulsive noises [14], [9], [6], [7], [10] defined by ϕ(e) and sparsity-aware penalties [19], [16], [18], [15], [95] defined by f (w) have been studied, which can be applied in the AOP-SA-RNSAF. Nevertheless, this paper does not consider the effect of different choices of ϕ(e) and/or f (w), which is worth studying in future work.…”

Section: B Adaptation Of the Sparsity Penalty Weightmentioning

confidence: 99%

“…Later, robust PNSAF algorithms were also presented [14], [10] to deal with impulsive noises. On the other hand, the family of sparsityaware algorithms incorporates the sparsity-aware penalty into the original NSAF's and PNSAF's cost functions; as a result, sparsity-aware NSAF (SA-NSAF) [15], [16] and sparsityaware PNSAF [17] algorithms were developed. In sparse system identification, these sparsity-aware algorithms can obtain better convergence and steady-state performance than their original counterparts.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization

Chen¹,

Huang²,

He³

et al. 2022

Preprint

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A Bayesian transfer sparse identification method for nonlinear ARX systems

Zhang,

Luan,

Ding

et al. 2024

Adaptive Control & Signal

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SummaryIn this paper, we design a transfer sparse identification algorithm under the Bayesian framework through introducing other system knowledge into the system to be identified. This method provides a new identification solution for a nonlinear autoregressive model with exogenous inputs (NARX). The estimates of the transferred parameters are calculated by adding the transfer correction term to the un‐transferred estimates. To achieve this, a joint prior distribution is devised for the parameters, ultimately enhancing the efficient utilization of existing data, reducing the reliance on new data, and achieving more accurate identification. The maximized marginal likelihood method is used to find the transfer gain and the transfer information matrix in the transfer correction term. Meanwhile, in order to make the algorithm automatically adapt to different data, we design an automatic structure detection method based on the transfer framework. The method automatically determines the sparsity threshold based on the maximum inter‐class variance. Two examples are provided to demonstrate the advantages of our algorithm.

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Improved multiband structured subband adaptive filter algorithm with L0-norm regularization for sparse system identification

Cited by 9 publications

References 23 publications

Sparsity-Aware Robust Normalized Subband Adaptive Filtering Algorithms With Alternating Optimization of Parameters

Sparsity-Aware Robust Normalized Subband Adaptive Filtering Algorithms With Alternating Optimization of Parameters

Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization

A Bayesian transfer sparse identification method for nonlinear ARX systems

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