When the input is a highly correlated and noisy signal over sensor networks, it will lead to the severe performance degeneration of traditional distributed algorithms in terms of convergence rate and steady-state error. To tackle such an issue, this paper proposes distributed bias-compensated separated-decorrelation least mean-square (BC-SDLMS) algorithms. Due to the adoption of new separated-decorrelation structure and bias-compensated term, the steady-state mean-square error with the proposed algorithms can be reduced in comparison with the previous decorrelation schemes. The mean-square analysis is also carried out, which indicates that the proposed algorithms can converge to an unbiased solution. Moreover, an effective estimate for the noise variance at the input terminal of every node is designed. Finally, simulation comparisons are made between the proposed BC-SDLMS algorithms and the competing methods in terms of convergence rate and steady-state error for different colored and noisy inputs.

中文翻译：

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使用自适应采样有效估计图信号

当输入是传感器网络上高度相关且有噪声的信号时，将导致传统分布式算法在收敛速度和稳态误差方面的严重性能退化。为了解决这个问题，本文提出了分布式偏置补偿分离去相关最小均方（BC-SDLMS）算法。由于采用了新的分离解相关结构和偏置补偿项，与以前的解相关方案相比，所提出算法的稳态均方误差可以降低。还进行了均方分析，这表明所提出的算法可以收敛到无偏解。此外，还设计了对每个节点输入端噪声方差的有效估计。最后，