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Robust Diffusion Total Least Mean M-estimate Adaptive Filtering Algorithm and Its Performance Analysis
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DOI：10.1109/TCSII.2021.3100422.png)
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Abstract
En 中文
In this brief, considering the dramatically deteriorating convergence behavior of the diffusion gradient-descent total least squares (DGDTLS) algorithm in the error-in-variables (EIV) model containing impulsive noise, a robust diffusion total least mean M-estimate (DTLMM) algorithm is proposed, which incorporates the M-estimate function (MF). The proposed algorithm significantly enhances the robustness of the DGDTLS algorithm in suppressing impulse noise. Under the impulse noise conditions, the mean condition and steady-state mean-square behaviors of the DTLMM algorithm are evaluated in detail. Algorithm simulations under different conditions are executed. The simulation results prove the robustness of the proposed algorithms and the consistency between the theoretical value and the simulated value.
Keywords:
Manganese
Steady-state
Robustness
Convergence
Stability analysis
Parameter estimation
Irrigation
Distributed estimation
total least squares method
impulsive noise
error-in-variables model
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