Robust Adaptation in Impulsive Noise

Al-Sayed, Sara; Zoubir, Abdelhak M.; Sayed, Ali H.

doi:10.1109/tsp.2016.2535239

Cited by 59 publications

(24 citation statements)

References 33 publications

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“…In Figure 12, the attenuation of FxLMS was inferior to that of other methods applied in this experiment when impulsive interference occurred, it diverged at about 11 s, which also confirmed the conclusion in [8][9][10][11][12][13][14][15] theoretical analysis that the FxLMS cannot cancel impulsive noise. Although the FxlogLMS could maintain the steady state after the impulsive interference, the average attenuation of it was inferior to that of PFxNLMS(Opt) and DMPFxNLMS.…”

Section: Methodssupporting

confidence: 83%

“…Based on this structure, several methods of M-estimate have been introduced into active impulsive noise control [6][7][8][9][10][11][12][13]. However, the main problem of applications of these methods is that it cannot works well in the variable-α impulsive noise environment, because these methods use a nonlinear transform function with a certain threshold.…”

Section: Preliminarymentioning

confidence: 99%

“…However, the parameter α is variable in the natural process, which may lead these methods to fail to converge. Therefore, M-estimate functions are developed for active impulsive noise control in [8][9][10][11]. These functions are non-linear transformation methods with different thresholds that are calculated by using prior information.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Adaptive State Detector-Based Post-Filtering Active Control Algorithm for Gaussian Noise Environment with Impulsive Interference

et al. 2019

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For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.

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Section: Methodssupporting

confidence: 83%

Section: Preliminarymentioning

confidence: 99%

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A Novel Adaptive State Detector-Based Post-Filtering Active Control Algorithm for Gaussian Noise Environment with Impulsive Interference

et al. 2019

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“…Distributed learning is a powerful technique for extracting information from networked agents (see, e.g., [2][3][4][5][6][7][8] and the references therein). In this work, we consider a network of agents connected by a graph.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Decentralized Clustering and Linking by Networked Agents

Khawatmi¹,

Sayed

Zoubir³

2017

IEEE Trans. Signal Process.

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We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We propose a decentralized clustering algorithm aimed at identifying and forming clusters of agents of similar objectives, and at guiding cooperation to enhance the inference performance. One key feature of the proposed technique is the integration of the learning and clustering tasks into a single strategy. We analyze the performance of the procedure and show that the error probabilities of types I and II decay exponentially to zero with the step-size parameter. While links between agents following different objectives are ignored in the clustering process, we nevertheless show how to exploit these links to relay critical information across the network for enhanced performance. Simulation results illustrate the performance of the proposed method in comparison to other useful techniques

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“…It has been proved that the error nonlinear function is optimal regarding the steady-state mean square error when the order of the error in the denominator of the weight update is one order larger than that of the numerator [6]. Therefore, q ∈ [0, 2] is chosen to ensure the stability and filtering accuracy of (4), which is also explained in [7,8]. According to (3), the error nonlinear function of (4) is rewritten as…”

mentioning

confidence: 99%