A Robust Total Least Mean M-Estimate Adaptive Algorithm for Impulsive Noise Suppression

Li, Lei; Lv, Shaohui

doi:10.1109/tcsii.2019.2925626

Cited by 35 publications

(5 citation statements)

References 23 publications

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“…Construct (t ), f (u(t )), F (t ) and (t ) using (40), (39), (38) and (34). Compute e(t ), (t ) and̂(t ) using (37), (36) and (35). 4.…”

Section: Robust Multi-innovation Gradient Algorithmmentioning

confidence: 99%

“…Stojanovic and Nedic modelled the non‐Gaussian noise as an

ε

‐contaminated distribution, and proposed a robust recursive algorithm for linear time‐varying output‐error systems by taking the expectation of least favorable probability density of prediction errors as the cost function [34]. Li and Zhao presented an M‐estimate function‐based total least mean algorithm for errors‐in‐variable systems, where a threshold parameter is designed to control the suppression of the impulsive noise [35]. Liu and Yang applied the expectation‐maximization algorithm to the identification of a non‐linear state‐space model, in which Student's

t

‐distribution is used to describe the non‐Gaussian noises with outliers [36].…”

Section: Introductionmentioning

confidence: 99%

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The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise

Wang

Ding

2021

IET Control Theory & Appl

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The characteristic of the external noise has significant influences on system modelling and identification, and the assumption that the noise follows the Gaussian distribution may be invalid due to realistic reasons. This paper discusses the identification issue of Hammerstein non-linear systems with non-Gaussian noise and presents a robust gradient algorithm. The algorithm is derived based on the logarithmic cost function of continuous mixed pnorm of prediction errors, which takes into account each p-norm of errors for 1 ⩽ p ⩽ 2. The gain at each recursive step adapts to the data quality so that the algorithm has good robustness to non-Gaussian noise. To improve the estimation accuracy, a robust multiinnovation gradient algorithm is proposed by using the multi-innovation identification theory. Two examples are provided to exhibit the validity of the proposed algorithms. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Construct (t ), f (u(t )), F (t ) and (t ) using (40), (39), (38) and (34). Compute e(t ), (t ) and̂(t ) using (37), (36) and (35). 4.…”

Section: Robust Multi-innovation Gradient Algorithmmentioning

confidence: 99%

“…Stojanovic and Nedic modelled the non‐Gaussian noise as an

ε

t

‐distribution is used to describe the non‐Gaussian noises with outliers [36].…”

Section: Introductionmentioning

confidence: 99%

The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise

Wang

Ding

2021

IET Control Theory & Appl

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“…Digital cancellation algorithms accounting for impulsive noise have been proposed in earlier research [ 10 , 17 ]. The work in [ 11 , 12 ] focused on linear SIC by employing the least mean square (LMS) adaptive filter.…”

Section: Introductionmentioning

confidence: 99%

Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System

Song,

Tang,

et al. 2024

Sensors

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Simultaneous transmit and receive wireless communications have been highlighted for their potential to double the spectral efficiency. However, it is necessary to mitigate self-interference (SI). Considering both the SI channel and remote transmission (RT) channel need to be estimated before equalizing the received signal, we propose two adaptive algorithms for linear and nonlinear self-interference cancellation (SIC), based on a multi-layered joint channel estimator structure. The proposed algorithms estimate the RT channel while performing SIC, and the multi-layered structure ensures improved performance across various interference-to-signal ratios. The M-estimate function enhances the robustness of the algorithm, allowing it to converge even when affected by impulsive noise. For nonlinear SIC, this paper introduces an adaptive algorithm based on generalized Hammerstein polynomial basis functions. The simulation results indicate that this approach achieves a better convergence speed and normalized mean squared difference compared to existing SIC methods, leading to a lower system bit error rate.

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“…The most important thing for adaptive algorithms is to choose a cost function, and the least mean square (LMS) algorithm enjoys widespread adoption in engineering applications, this algorithm is favored for its straightforwardness and straightforward implementation [7]. However, using the second-order statistics of the error as the cost function performs poorly in non-Gaussian noise environments and often requires combining other methods to achieve better robustness in practical scenarios with impulsive noise [8][9][10]. There are many marine organisms, such as snapping shrimp, that can emit impulsive noise [11], which causes underwater acoustic channels to be frequently affected by impulsive noise.…”

Section: Introductionmentioning

confidence: 99%

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

Zhu,

Wang,

Cai

et al. 2024

Electronics

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Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm.

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A Robust Total Least Mean M-Estimate Adaptive Algorithm for Impulsive Noise Suppression

Cited by 35 publications

References 23 publications

The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise

The robust multi‐innovation estimation algorithm for Hammerstein non‐linear systems with non‐Gaussian noise

Digital Self-Interference Canceler with Joint Channel Estimator for Simultaneous Transmit and Receive System

A Convex Combination–Variable-Step-Size Least Mean p-Norm Algorithm

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