Widely Linear Complex-Valued Least Mean M-Estimate Algorithms: Design and Performance Analysis

Li, Lei; Pu, Yi-Fei

doi:10.1007/s00034-022-02053-z

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…S IGNALS are often contaminated by unwanted artifacts and noise that affect the performance of adaptive filters. Such filters, originally derived from the optimal prediction and filtering method for solving the Wiener-Hoff equation, play a relevant role in advanced signal processing and control schemes [1], [2]. The optimization problem they solve is intrinsically related to their learning features, such as stability, computational cost and robustness against impulsive noise that may be found in the measurement noise.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm operates in some scenarios in an attractive point of the ubiquitous trade-off between convergence rate, asymptotic performance and computational burden. Namely, the method blends the reduction of computational cost from the Set-membership approach (due to its data censoring capabilities) with the robustness against impulsive noise provided by the signed-error strategies 1 . It is noteworthy that such a reduction is crucial in applications that demand thousands of adaptive taps (e.g., in the acoustic echo cancellation task [7]).…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Modeling of the Set-Membership- Sign-NLMS Algorithm

De Souza,

Henriques,

Siqueira

et al. 2024

IEEE Access

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This paper proposes the Set-Membership sign-NLMS (SM-sign-NLMS) adaptive filter, which combines the ability for data censoring (offered by Set-Membership schemes) with robustness against impulsive noise (provided by signed schemes). The algorithm can present a much lower steady-state probability of update than the standard SM-NLMS algorithm when impulsive noise is present in the system. It is derived from a local deterministic optimization problem modulated by a minimum disturbance cost function combined with a bounded error criterion. Several stochastic models are proposed in order to extract insights and a time-variant step size extension of the algorithm. The first of them, based on energy conservation arguments, leads to a fixed-point analytic equation whose solution predicts the asymptotic performance of the algorithm. Further, a transient analysis based on a statistical decoupling of the radial and (discrete) angular distributions of the input vector is derived. Based on such an analysis, an efficient time-variant step-size version of the algorithm is proposed. Additionally, such an analysis is also utilized to obtain a fixed-point formula whose solution describes the asymptotic performance when the unknown plant that the filter intends to match varies according to a first-order Markovian model. Lastly, a novel stochastic model is advanced for the description of the algorithm learning behavior under a deficient-length scenario for a white input signal, which provides some insights about the asymptotic performance of the algorithm. The findings are confirmed by extensive simulations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%