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QR-based incremental minimum-Wilcoxon-norm strategies for distributed wireless sensor networks

机译：基于QR的分布式无线传感器网络增量式最小Wilcoxon范式策略

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摘要

When outliers are present in the desired data, the conventional distributed adaptive estimation algorithms exhibit poor performance. To alleviate this shortcoming a novel distributed robust incremental strategy based on QR decomposition and the Wilcoxon score is proposed which convergence speed is faster than the other previous techniques. To demonstrate the potential of this algorithm simulation study is carried out for the distributed estimation of parameters in the presence of weak to strong outliers in the desired data. The results show that the performance of the new algorithm is robust against outliers compared to conventional incremental RLS algorithm. Further to achieve low communication overhead, a new scheme is introduced and its performance has been assessed through simulation study. It is observed that the proposed scheme even though exhibits slightly inferior performance but offers substantially reduction in terms of communication overhead.
机译：当期望数据中存在异常值时，常规的分布式自适应估计算法将表现出较差的性能。为了减轻这一缺点，提出了一种基于QR分解和Wilcoxon评分的新颖的分布式鲁棒增量策略，其收敛速度比其他现有技术更快。为了证明该算法的潜力，在所需数据中存在从弱到强离群值的情况下，对参数的分布式估计进行了仿真研究。结果表明，与传统的增量RLS算法相比，该新算法的性能对异常值具有鲁棒性。为了实现较低的通信开销，引入了一种新方案，并通过仿真研究对其性能进行了评估。可以看出，所提出的方案即使表现出稍差的性能，但是在通信开销方面却大大降低了。

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