Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Tian, Baofeng; Zhou, Yuanyuan; Zhu, Hui; Jiang, Chuandong; Yi, Xiaofeng

doi:10.1007/s11771-017-3492-y

Cited by 5 publications

(2 citation statements)

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“…Four classes of adaptive algorithms are considered in this paper. The adaptive algorithms are: least mean square (LMS), normalized LMS (NLMS), sigmoid variable size LMS (SVSLMS) [37,38], and regularized variable step size (VSS) techniques: regularized variable step size LMS (RVSSLMS) and regularized sigmoid variable size LMS (RSVSLMS). The expression for the calculation of the weight coefficients for the different LMS techniques to be considered is given as, LMS:…”

Section: Fixed and Step-size Lms Algorithmsmentioning

confidence: 99%

“…Therefore, for the comparison of the function curve from other SVSLMS algorithm, we set α and λ to be 5 and 0.5, respectively, considering the demands of larger initial step size and a steady change when e(n) is close to 0. Figure 3 shows the curves of the step size, µ(n) changing with the error function, e(n), adopting methods from the modified SVSLMS algorithm (blue dot/scattered plot) for comparison [38]. The proposed RSVSLMS algorithm has a large but slowly varying step size in the initial phase to ensure rapid convergence rate, and after convergence, the step size varies slowly in a steady-state phase.…”

Section: Rsvslms Algorithm 1 Svslms Algorithm Rvsslms Algorithm Rsvsl...mentioning

confidence: 99%

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Adaptive Processing for EM Telemetry Signal Recovery: Field Data from Sichuan Province

Fayemi

Zhen

et al. 2020

Energies

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This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals.

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Section: Fixed and Step-size Lms Algorithmsmentioning

confidence: 99%

Section: Rsvslms Algorithm 1 Svslms Algorithm Rvsslms Algorithm Rsvsl...mentioning

confidence: 99%