传感器网络分布式顽健RLS估计算法

康凯凯; 刘兆霆; 姚英彪

doi:10.11959/j.issn.1000−0801.2019002

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传感器网络分布式顽健RLS估计算法

研究与开发 | 更新时间：2024-06-05

- 传感器网络分布式顽健RLS估计算法
- Distributed robust RLS estimation algorithm over sensor networks
- 电信科学 2019年35卷第1期页码：37-43
- 作者机构：
- 作者简介：
  
  [ "康凯凯（1992− ），男，杭州电子科技大学通信学院硕士生，主要研究方向为分布式自适应信号处理。" ]
  [ "刘兆霆（1975− ），男，博士，杭州电子科技大学通信学院副教授、硕士生导师，主要研究方向为统计信号处理和传感器网络。" ]
  [ "姚英彪（1976− ），男，博士，杭州电子科技大学通信学院教授、硕士生导师，主要研究方向为媒体信号处理、无线传感器网络和固态存储。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(No.61671192);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(No.LY16F010012)
- DOI：10.11959/j.issn.1000−0801.2019002
  中图分类号： TP393
- 网络出版日期：2019-01，
  
  纸质出版日期：2019-01-20
- 稿件说明：
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康凯凯, 刘兆霆, 姚英彪. 传感器网络分布式顽健RLS估计算法[J]. 电信科学, 2019,35(1):37-43.

Kaikai KANG, Zhaoting LIU, Yingbiao YAO. Distributed robust RLS estimation algorithm over sensor networks[J]. Telecommunications science, 2019, 35(1): 37-43.
康凯凯, 刘兆霆, 姚英彪. 传感器网络分布式顽健RLS估计算法[J]. 电信科学, 2019,35(1):37-43. DOI： 10.11959/j.issn.1000−0801.2019002.

Kaikai KANG, Zhaoting LIU, Yingbiao YAO. Distributed robust RLS estimation algorithm over sensor networks[J]. Telecommunications science, 2019, 35(1): 37-43. DOI： 10.11959/j.issn.1000−0801.2019002.

摘要

针对在传感器网络的参数估计中，测量环境可能存在冲击噪声或脉冲干扰，导致测量值中包含大大偏离实际范围的离群值，从而无法获得有效的参数估计问题，提出了一种基于节点协作、具有顽健性的分布式RLS估计算法，该算法利用在一段时间内，节点受到干扰或攻击通常具有偶发性（即稀疏性）的特点，在代价函数中引入ℓ

范数，对测量数据中可能存在的离群值进行识别和剔除，同时利用网络各节点之间的相互协作，进一步提高参数估计的性能。通过计算机仿真实验，验证了该算法具有良好的估计性能。

Abstract

In the parameter estimation of the sensor network

there may be impact noise or pulse interference in the measurement environment

which leads to the outliers that greatly deviate from the actual range

so that the effective parameter estimation problem can't be obtained. A distributed RLS estimation algorithm based on node cooperation and robustness was proposed. The characteristic of interference or attack on a node is usually sporadic (ie

sparse) for a period of time was utilized

ℓ

norm was introduced to cost function. The outliers which may exist in the measurement data were identified and eliminated

and the interaction between the nodes of the network was utilized to further improve the performance of the parameter estimation. The computer simulation experiments show that the algorithm has good estimation performance.

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references

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