Normalized Subband Adaptive Filtering Algorithm With Reduced Computational Complexity

Petraglia, M.R.; Haddad, Diego B.; Marques, Elias L.

doi:10.1109/tcsii.2015.2468952

Cited by 20 publications

(12 citation statements)

References 19 publications

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“…The measurement noise

ν_{normalM} false(k false)

is a zero‐mean i.i.d. stochastic process, which is statistically independent from the input signal.Remark 1 NA is a standard assumption in analyses of adaptive filtering algorithms, and often is satisfied in practice [24, 25]. In this Letter, the overall noise

ν false(k false)

is neither i.i.d.…”

Section: Exact Expectation Analysis Of Lms‐based Nls Identificationmentioning

confidence: 99%

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Exact expectation analysis of the LMS adaptive identification of non‐linear systems

et al. 2020

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Most adaptive filtering schemes employ the tapped-delay line. In part, such a fact can be explained by the assumption that the plant they intend to estimate is linear. Although such a hypothesis can be reasonable if the input signal is constrained to a certain range, sometimes it may not be valid. In this last case, the performance and stability guarantees provided by stochastic models that presume linearity of the ideal system are no longer valid. This Letter advances an analytic model of the least-mean-square (LMS) learning capabilities when the ideal system is not linear, with the additive noise including nonlinear functions of the input samples. The proposed analysis does not assume neither that the excitation signal is statistically independent of the adaptive weights, nor that the additive noise is white and/or independent of input data. Furthermore, it can be applied to non-Gaussian and/or non-white input signals. Simulations show that the advanced model is more accurate than traditional approaches. CAPES, CNPq, and FAPERJ for supporting this research.

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“…The measurement noise

ν_{normalM} false(k false)

ν false(k false)

is neither i.i.d.…”

Section: Exact Expectation Analysis Of Lms‐based Nls Identificationmentioning

confidence: 99%

“…Remark 1: NA is a standard assumption in analyses of adaptive filtering algorithms, and often is satisfied in practice [24,25]. In this Letter, the overall noise n(k) is neither i.i.d.…”

mentioning

confidence: 94%

Exact expectation analysis of the LMS adaptive identification of non‐linear systems

et al. 2020

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“…In the NSAF design, the principle of minimum disturbance is also applied, and the update design is thus considered a solution of a multiple constraints optimisation problem. The goal of the NSAF using sparse filters (NSAF‐SF) is to preserve the convergence rate while reducing the computational complexity [4]. This goal is achieved by using the same premises of the NSAF algorithm ( i.e.…”

Section: Introductionmentioning

confidence: 99%

“…T is the input vector. The NSAF-SF algorithm [4] derives from the closed-loop subband structure depicted in the dashed rectangle of Fig. 1.…”

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confidence: 99%

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Improved sparsity‐aware NSAF‐SF adaptive algorithm

et al. 2020

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Normalised subband adaptive filtering algorithms have attracted attention due to their ability to present faster convergence in the case of coloured excitation data. The NSAF-SF scheme is an example of a state-of-the-art subband adaptive algorithm that demands a low computational burden. This Letter proposes a deterministic local optimisation approach with affine constraints whose result leads to an enhanced NSAF-SF updating mechanism. One of these constraints consists of a projection into a hyperplane derived from a relaxed ℓ 1-norm restriction, which provides a sparsity-promoting scheme. Such a constraint incorporates prior information into the (originally sparsity-agnostic) NSAF-SF. Such prior information concerns the energy concentration of the ideal transfer function that the adaptive filter intends to identify. Furthermore, the advanced optimisation problem is modified in order to make the advanced adaptive filter robust against impulsive noise. The simulations present a performance improvement for both transient and steady-state regions.

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On the Performance Analysis of Normalized Subband Adaptive Filtering Algorithm with Sparse Subfilters

Xavier

Haddad

Petraglia

2020

Circuits Syst Signal Process

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Normalized Subband Adaptive Filtering Algorithm With Reduced Computational Complexity

Cited by 20 publications

References 19 publications

Exact expectation analysis of the LMS adaptive identification of non‐linear systems

Exact expectation analysis of the LMS adaptive identification of non‐linear systems

Improved sparsity‐aware NSAF‐SF adaptive algorithm

On the Performance Analysis of Normalized Subband Adaptive Filtering Algorithm with Sparse Subfilters

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