Resilient Distributed Field Estimation
Resilient Distributed Field Estimation
复制标题
DOI：
10.1137/19m1256567
复制
发表时间：
2019-04
期刊：
影响因子：
--
通讯作者：
Yuan Chen;S. Kar;J. Moura
中科院分区：
文献类型：
--
作者：
Yuan Chen;S. Kar;J. Moura
研究了测量攻击下的弹性分布场估计。代理或设备的网络测量大的、空间分布的物理场参数。对手任意操纵一些代理的测量。每个代理的目标是处理从其邻居接收到的测量和信息，以仅估计场的几个特定分量。我们提出了$\mathbf{SAFE}$，饱和自适应字段估计，一个共识+创新的分布式字段估计，是弹性的测量攻击。在充分的条件下妥协的测量流，现场和代理的测量之间的物理耦合，以及网络通信网络的连通性，$\mathbf{SAFE}$保证每个代理的估计几乎肯定收敛到代理感兴趣的参数的组件的真实值。最后，我们通过数值例子来说明$\mathbf{SAFE}$的性能。
We study resilient distributed field estimation under measurement attacks. A network of agents or devices measures a large, spatially distributed physical field parameter. An adversary arbitrarily manipulates the measurements of some of the agents. Each agent's goal is to process its measurements and information received from its neighbors to estimate only a few specific components of the field. We present $\mathbf{SAFE}$, the Saturating Adaptive Field Estimator, a consensus+innovations distributed field estimator that is resilient to measurement attacks. Under sufficient conditions on the compromised measurement streams, the physical coupling between the field and the agents' measurements, and the connectivity of the cyber communication network, $\mathbf{SAFE}$ guarantees that each agent's estimate converges almost surely to the true value of the components of the parameter in which the agent is interested. Finally, we illustrate the performance of $\mathbf{SAFE}$ through numerical examples.