Robust Diffusion Total Least Mean M-estimate Adaptive Filtering Algorithm and Its Performance Analysis

Zhao, Haiquan; Chen, Yida; Lv, Shaohui

doi:10.1109/tcsii.2021.3100422

Cited by 10 publications

(2 citation statements)

References 19 publications

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“…effect of impulsive noise on the filter weights [102], [103], [104], [105]. Such algorithms are essentially hybrid techniques based on L 1 and L 2 -norms and several variants have been presented [106], [107], [108]. However, these algorithms can only deal with impulsive noise and cannot achieve improved performance in Gaussian scenarios.…”

Section: Introductionmentioning

confidence: 99%

Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate

Lu¹,

Chen²,

Lamare³

et al. 2022

Preprint

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We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications.

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Section: Introductionmentioning

confidence: 99%

Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate

Lu¹,

Chen²,

Lamare³

et al. 2022

Preprint

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show abstract

“…When a compensation error occurs, the performance will also degenerate. In addition, a robust diffusion total least mean M-estimate adaptive filtering algorithm is proposed for diffusion network [42].…”

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confidence: 99%

Maximum Total Improper Complex Correntropy Algorithm for Widely Linear Adaptive Filtering

Fu¹,

Li²

2022

IEEE Access

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The maximum total complex correntropy (MTCC) algorithm improves the performance of adaptive filtering under the error in variable (EIV) model by integrating both input and output noise information into the total complex correntropy. However, the MTCC algorithm cannot be applied to the widely linear model, directly. Compared with a strictly linear model, the widely linear model provides the complete statistical information of signals for adaptive filtering. This paper first constructs a novel cost function for the widely linear model by incorporating the input and output noise information into the improper complex correntropy. Then, a novel widely linear adaptive filter algorithm named widely linear maximum total improper complex correntropy (WL-MTICC) is proposed using the gradient descent method under the maximum total improper correntropy criterion. Finally, the analysis of local stability and convergence in the mean sense regarding the proposed WL-MTICC algorithm is provided. Simulations are used to show the performance advantage of the proposed WL-MTICC algorithm in the presence of both input and output noises.INDEX TERMS adaptive filter, convergence, stability, total complex correntropy, widely linear model. I. INTRODUCTIONSince adaptive filtering adjusts the filter parameters flexibly by combining the input and expected signals, it has been developed well in signal processing, control, communication, and other scenarios [1-2]. The original adaptive filtering algorithm aimed at the real-valued field, and its classical representative is the least-mean-square (LMS) [3][4][5] algorithm. LMS has the good properties of fast convergence and simple structure. Researchers have made certain improvements in developing a series of LMS algorithm variations to further improve the convergence performance and steady-state performance of LMS in a Gaussian environment [6][7][8][9]. In addition, to improve the performance of LMS type algorithms for colored inputs, affine projection algorithm (APA) and recursive least-squares (RLS) were also developed in [10][11]. However, these algorithms are not robust to non-Gaussian noises. By utilizing the kernel function, researchers have developed a series of adaptive filtering algorithms based on correntropy in [12][13][14][15][16][17][18][19][20][21] to make adaptive filtering algorithms robust in a non-Gaussian noise environment. In addition, some correntropy based spare adaptive filtering algorithms have also been proposed in [22][23][24][25], such as general zero attraction proportionate normalized maximum correntropy criterion (GZA-PNMCC) [22], correntropy induced metric maximum correntropy criterion (CIMMCC) [23], and group-constrained maximum correntropy criterion (GC-MCC) [24].Since signals are represented in the complex-valued form in many scenarios, adaptive filtering in the complex-valued domain attracts considerable critical attention of researchers, then a series of adaptive filtering algorithms are followed, including complex LMS (CLMS) algorithm [26] and complex correntr...

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Modified Huber M-Estimate Function-Based Distributed Constrained Adaptive Filtering Algorithm Over Sensor Network

Zhao

Zhou

2022

IEEE Sensors J.

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Robust Diffusion Total Least Mean M-estimate Adaptive Filtering Algorithm and Its Performance Analysis

Cited by 10 publications

References 19 publications

Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate

Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate

Maximum Total Improper Complex Correntropy Algorithm for Widely Linear Adaptive Filtering

Modified Huber M-Estimate Function-Based Distributed Constrained Adaptive Filtering Algorithm Over Sensor Network

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