SIAM Journal on Control and Optimization, Volume 58, Issue 3, Page 1429-1456, January 2020.
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 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, 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 SAFE through numerical examples.

中文翻译：

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弹性分布式场估计

SIAM控制与优化杂志，第58卷，第3期，第1429-1456页，2020年1月。
我们研究了测量攻击下的弹性分布式现场估计。代理或设备网络可测量较大的，空间分布的物理场参数。对手任意操纵某些特工的度量。每个代理的目标是处理其测量值和从其邻居接收的信息，以仅估计该字段的一些特定部分。我们提出了SAFE（饱和自适应场估计器），它是一种对测量攻击具有弹性的共识+创新型分布式场估计器。在受损的测量流，现场与代理商的度量之间的物理耦合以及网络通信网络的连通性的充分条件下，SAFE保证每个代理商的 估计几乎可以肯定地收敛到代理感兴趣的参数组成部分的真实值。最后，我们通过数值示例说明SAFE的性能。