This paper investigates distributed estimation of an unknown vector parameter in adversarial environments. Individual agents make successive local measurements of the unknown parameter and aim at estimating the unknown parameter consistently by sharing these measurements with their neighbors over a time-varying directed communication graph even when some of the agents are under attacks and their measurements are manipulated arbitrarily. To this end, we design push-sum-based recursive algorithms to estimate the unknown parameter for linear and nonlinear measurement models, respectively. It is demonstrated that the presented algorithms can ensure that the local estimates at all the agents converge to the true value of the parameter under some mild assumptions, such as, $B$ -strong-connectedness of the communication topologies and a topology-independent constraint on the number of compromised measurements. A numerical example is presented to illustrate the effectiveness of the proposed algorithms.

中文翻译：

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传感器攻击下的分布式估计：线性和非线性测量模型

本文研究了对抗环境中未知向量参数的分布式估计。单个代理对未知参数进行连续的本地测量，并且即使在某些代理受到攻击并且可以任意操纵其测量的情况下，也可以通过在时变有向通信图上与邻居共享这些测量，从而始终如一地估计未知参数。为此，我们设计了基于推和的递归算法来分别估计线性和非线性测量模型的未知参数。证明了所提出的算法可以确保在某些温和的假设下，所有代理的局部估计收敛到参数的真实值，例如，$ B $ 通信拓扑的强连通性和对受害测量数量的拓扑独立约束。数值例子说明了所提算法的有效性。