Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Guo, Ying; Li, Jingjing; Li, Yingsong

doi:10.3390/sym11111335

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…Due to the merits of adaptive filter [68][69][70][71], the graph sampling with adaptive strategy has been proposed and developed in [36,[72][73][74][75][76]. Specifically, Lorenzo et al proposed an adaptive method to reconstruct graph signals by exploiting the use of the least mean squares (LMS) strategy [72].…”

Section: Graph Sampling With Adaptive Strategymentioning

confidence: 99%

Robust Adaptive Estimation of Graph Signals Based on Welsch Loss

Wang

Sun

2022

Symmetry

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This paper considers the problem of adaptive estimation of graph signals under the impulsive noise environment. The existing least mean squares (LMS) approach suffers from severe performance degradation under an impulsive environment that widely occurs in various practical applications. We present a novel adaptive estimation over graphs based on Welsch loss (WL-G) to handle the problems related to impulsive interference. The proposed WL-G algorithm can efficiently reconstruct graph signals from the observations with impulsive noises by formulating the reconstruction problem as an optimization based on Welsch loss. An analysis on the performance of the WL-G is presented to develop effective sampling strategies for graph signals. A novel graph sampling approach is also proposed and used in conjunction with the WL-G to tackle the time-varying case. The performance advantages of the proposed WL-G over the existing LMS regarding graph signal reconstruction under impulsive noise environment are demonstrated.

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Section: Graph Sampling With Adaptive Strategymentioning

confidence: 99%

Robust Adaptive Estimation of Graph Signals Based on Welsch Loss

Wang

Sun

2022

Symmetry

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“…where the window length of the error samples is L. From [17], it can be seen that the gradient-based approach can be used to solve the optimization problem in Equation ( 11) in a given finite set. In this work, the following simple methods are used iteratively to optimize the free parameters c l and τ l : Center c: The mean or median value of the error samples are used to obtain the estimate of the center parameter c, and the method can be given as:…”

Section: Free Parameter Optimizationmentioning

confidence: 99%

“…In addition, a proportional DMCC algorithm with adaptable kernel width was proposed for sparse distributed system identification in the cases of impulse noise [16]. The diffusion subband adaptive filtering (DSAF) algorithm, based on symmetrical MCC with individual weighting factors, was developed for colored input signals [17]. To improve the convergence performance of the conventional diffusion affine projection (AP) algorithm, an MCC-based diffusion AP algorithm was further derived using the MCC as the cost function for DE over networks [18].…”

Section: Introductionmentioning

confidence: 99%

Diffusion Generalized MCC with a Variable Center Algorithm for Robust Distributed Estimation

Cai

Sun

et al. 2021

Electronics

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Classical adaptive filtering algorithms with a diffusion strategy under the mean square error (MSE) criterion can face difficulties in distributed estimation (DE) over networks in a complex noise environment, such as non-zero mean non-Gaussian noise, with the object of ensuring a robust performance. In order to overcome such limitations, this paper proposes a novel robust diffusion adaptive filtering algorithm, which is developed by using a variable center generalized maximum Correntropy criterion (GMCC-VC). Generalized Correntropy with a variable center is first defined by introducing a non-zero center to the original generalized Correntropy, which can be used as robust cost function, called GMCC-VC, for adaptive filtering algorithms. In order to improve the robustness of the traditional MSE-based DE algorithms, the GMCC-VC is used in a diffusion adaptive filter to design a novel robust DE method with the adapt-then-combine strategy. This can achieve outstanding steady-state performance under non-Gaussian noise environments because the GMCC-VC can match the distribution of the noise with that of non-zero mean non-Gaussian noise. The simulation results for distributed estimation under non-zero mean non-Gaussian noise cases demonstrate that the proposed diffusion GMCC-VC approach produces a more robustness and stable performance than some other comparable DE methods.

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“…In addition, λ(n) = 0 in the transient state and λ(n) = 1(n → ∞) in the steady state, such that µ 1 or µ 2 , should also satisfy condition (36). Also, from [19], we know that 0 < µ 2 , µ 1 1, so the ranges of µ s12 , µ β12 , and µ sβ12 are restricted as 0 < µ s12 , µ β12 , µ sβ12 1.…”

Section: Steady-state Analysismentioning

confidence: 99%

“…System identifications, including channel estimation, seismic system identification, noise or echo cancellation, and image recovery have been successfully realized by adaptive filters (AFs) [1]. Among conventional AF algorithms, the least-mean-square (LMS) and normalized LMS (NLMS) algorithms play important roles due to their low computational complexity and mathematical tractability [2,3].…”

Section: Introductionmentioning

confidence: 99%

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

Zhang

Zhao

2020

Symmetry

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In this paper, to further improve the filtering performance and enhance the poor tracking capability of the conventional combined step-size affine projection sign algorithm (CSS-APSA) in system identification, we propose a simplified CSS-APSA (SCSS-APSA) by applying the first-order Taylor series expansion to the sigmoidal active function (of which the independent variable is symmetric) of CSS-APSA. SCSS-APSA has lower computational complexity, and can achieve comparable, or even better filtering performance than that of CSS-APSA. In addition, we propose a modification of the sigmoidal active function. The modified sigmoidal active function is a form of scaling transformation based on the conventional one. Applying the modified function to the CSS-APSA, we can obtain the modified CSS-APSA (MCSS-APSA). Moreover, the extra parameter of MCSS-APSA provides the power to accelerate the convergence rate of CSS-APSA. Following the simplification operations of SCSS-APSA, the computational complexity of MCSS-APSA can also be reduced. Therefore, we get the simplified MCSS-APSA (SMCSS-APSA). Simulation results demonstrate that our proposed algorithms are able to achieve a faster convergence speed in system identification.

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Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Cited by 4 publications

References 38 publications

Robust Adaptive Estimation of Graph Signals Based on Welsch Loss

Robust Adaptive Estimation of Graph Signals Based on Welsch Loss

Diffusion Generalized MCC with a Variable Center Algorithm for Robust Distributed Estimation

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

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