Error Saturation Nonlinearities for Robust Incremental LMS over Wireless Sensor Networks
Error Saturation Nonlinearities for Robust Incremental LMS over Wireless Sensor Networks
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DOI：
10.1145/2629667
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
T. Panigrahi;G. Panda;B. Mulgrew
中科院分区：
文献类型：
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作者：
T. Panigrahi;G. Panda;B. Mulgrew
传感器节点在地理区域上收集的数据被高斯和脉冲噪声污染。传统的基于梯度的分布式自适应估计算法在高斯噪声环境中表现出良好的性能，但在脉冲噪声环境中表现不佳。因此，本文的目的是提出一个强大的分布式自适应算法，消除脉冲噪声的影响。提出了一种基于误差饱和非线性的增量合作网络鲁棒分布式策略，用于脉冲噪声环境下的期望参数估计。利用时空能量守恒原理对所提出的误差饱和非线性增量最小均方（SNILMS）算法进行了稳态分析。理论和仿真结果都表明，误差非线性的存在使得所提出的SNILMS算法对脉冲噪声具有鲁棒性。
The data collected by sensor nodes over a geographical region is contaminated with Gaussian and impulsive noise. The conventional gradient-based distributed adaptive estimation algorithms exhibit good performance in the presence of Gaussian noise but perform poorly in impulsive noise environments. Therefore, the objective of this article is to propose a robust distributed adaptive algorithm that alleviates the effect of impulsive noise. An error saturation nonlinearity-based robust distributed strategy is proposed in an incremental cooperative network to estimate the desired parameters in impulsive noise. The steady-state analysis of the proposed error saturation nonlinearity incremental least mean squares (SNILMS) algorithm is carried out by employing the spatial-temporal energy conservation principle. Both theoretical and simulation results show that the presence of the error nonlinearity has made the proposed SNILMS algorithm robust to impulsive noise.