arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2306.02017eess.SYcs.SY
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传感器网络中的弹性分布式参数估计

Resilient Distributed Parameter Estimation in Sensor Networks

Jiaqi Yan, Kuo Li, Hideaki Ishii

AI总结:

针对传感器测量与更新可能被任意篡改的分布式参数估计问题,本文借助动态回归器扩展与混合过程,并提出弹性先组合后自适应扩散算法,保证正常传感器在低计算成本下指数收敛至真实参数。

摘要:

本文研究传感器网络中的参数估计问题,其中部分传感器的测量值和更新可能遭到对手的任意篡改。尽管存在此类不当行为,正常运行的传感器仍会对一个未知的 $d$ 维向量参数进行连续观测,并旨在通过在有向通信图上与邻居协作来推断其真实值。为此,我们利用所谓的动态回归器扩展与混合(dynamic regressor extension and mixing)过程,将估计向量参数的问题转化为估计 $d$ 个标量参数的问题。针对每个标量问题,我们提出一种弹性的“先组合后自适应”扩散算法:每个正常传感器执行弹性组合,以丢弃其邻域中的可疑估计并融合其余值,同时通过自适应步骤处理其流式观测。该估计器计算成本较低,并且即使部分正常传感器未受到充分激励,也能保证每个正常传感器以指数方式推断出真实参数。

英文摘要:

In this paper, we study the problem of parameter estimation in a sensor network, where the measurements and updates of some sensors might be arbitrarily manipulated by adversaries. Despite the presence of such misbehaviors, normally behaving sensors make successive observations of an unknown $d$-dimensional vector parameter and aim to infer its true value by cooperating with their neighbors over a directed communication graph. To this end, by leveraging the so-called dynamic regressor extension and mixing procedure, we transform the problem of estimating the vector parameter to that of estimating $d$ scalar ones. For each of the scalar problem, we propose a resilient combine-then-adapt diffusion algorithm, where each normal sensor performs a resilient combination to discard the suspicious estimates in its neighborhood and to fuse the remaining values, alongside an adaptation step to process its streaming observations. With a low computational cost, this estimator guarantees that each normal sensor exponentially infers the true parameter even if some of them are not sufficiently excited.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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