Modified Least-Mean Mixed-Norm Algorithms For Adaptive Sparse System Identification Under Impulsive Noise Environment

Rakesh, P.; Kumar, T. K. Satish; Albu, Felix

doi:10.1109/tsp.2019.8768813

Cited by 16 publications

(13 citation statements)

References 17 publications

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“…In [31], the fourth degree criterion was replaced with the least absolute deviations criterion, which made it possible to ensure the robustness of the obtained estimates under impulse interference conditions. The normalized modification of the identification algorithm considered in [32] was studied in [33,34], where the presence of impulse interference was also taken into account.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Robust identification of non-stationary objects with nongaussian interference

Rudenko¹,

Bezsonov²,

Lebediev³

et al. 2019

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Note that to ensure robustness of the obtained estimates, it is quite effective to use a combined learning functional [31,32]…”

Section: Obtaining Analytical Estimates Of Convergence Of Robust Idenmentioning

confidence: 99%

Robust identification of non-stationary objects with nongaussian interference

Rudenko¹,

Bezsonov²,

Lebediev³

et al. 2019

EEJET

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“…Works [14][15][16][17][18] developed a simpler approach to building combined functionals, consisting of both quadric and modular, and without the specified flaw.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Such a criterion was for the first time proposed in [14]. In [14][15][16][17][18], this criterion was used to solve the problem of identification in the presence of pulse interference. The stability of the normalized algorithm was studied in [15]; the applied identification problem was solved in [16].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In [14][15][16][17][18], this criterion was used to solve the problem of identification in the presence of pulse interference. The stability of the normalized algorithm was studied in [15]; the applied identification problem was solved in [16]. An adaptive combination of normalized filters was proposed in [17]; the convergence of the identification algorithm was studied in [18], where the task of selecting the optimal parameters' values of the algorithm was addresses.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Studying the properties of a robust algorithm for identifying linear objects, which minimizes a combined functional

Rudenko

Bezsonov

Lebediev

et al. 2020

EEJET

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This paper addresses the task of identifying the parameters of a linear object in the presence of non-Gaussian interference. The identification algorithm is a gradient procedure for minimizing the combined functional. The combined functional, in turn, consists of the fourth-degree functional and a modular functional, whose weights are set using a mixing parameter. Such a combination of functionals makes it possible to obtain estimates that demonstrate robust properties. We have determined the conditions for the convergence of the applied procedure in the mean and root-mean-square measurements in the presence of non-Gaussian interference. In addition, expressions have been obtained to determine the optimal values of the algorithm's parameters, which ensure its maximum convergence rate. Based on the estimates obtained, the asymptomatic and non-asymptotic values of errors in estimating the parameters and identification errors. Because the resulting expressions contain a series of unknown parameters (the values of signal and interference variances), their practical application requires that the estimates of these parameters should be used. We have investigated the issue of stability of the steady identification process and determined the conditions for this stability. It has been shown that determining these conditions necessitates solving the third-degree equations, whose coefficients depend on the specificity of the problem to be solved. The resulting ratios are rather cumbersome but their simplification allows for a qualitative analysis of stability issues. It should be noted that all the estimates reported in this work depend on the choice of a mixing parameter, the task of determining which remains to be explored. The estimates obtained in this paper allow the researcher to pre-evaluate the capabilities of the identification algorithm and the effectiveness of its use in solving practical problems

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