有适应力的分布式状态估计方法

为提高智能体系统对攻击的免疫力,研究了测量攻击下的适应力分布式状态估计方法。每个智能体对系统状态进行连续的本地线性测量。由于不同智能体的本地测量模型相互异构,对系统状态可能不具有本地可观测性,且攻击者能够操控部分智能体的测量数据,随意改变其测量结果。而智能体的目标是协同处理本地测量数据,并正确估计出未知的系统状态。因此,该问题的挑战在于在不对真实测量数据和恶意智能体的测量数据进行分辨时,如何设计算法估计得到真实的系统状态。为了解决这个问题,设计了适应性分布式最大后验概率估计算法。在该算法中,只要恶意智能体的数量小于某个特定值,所有智能体都能够收敛到系统状态。首先,根据卡尔曼滤波给出集中式最大后验概率(Maximum A Posteriori,MAP)估计方法,并与分布式一致性结合,进而得到分布式最大后验概率估计方法。然后,考虑到测量攻击,从估计一致性的角度,利用自适应饱和度增益设计了适应性分布式最大后验概率估计方法。最后,通过仿真实验验证算法的有效性。

Resilient Distributed State Estimation Algorithm

In order to improve the immunity of multi-agent system against attack,resilient distributed state estimation under measurement attacks is studied.Each agent makes successive local linear measurements of the system state.The local measurement models are heterogeneous across agents and may be locally unobservable for the system state.An adversary compromises some of the measurement streams and changes their values arbitrarily.The agents’goal is to cooperate with their local measurements and estimate the value of the system state correctly.The challenge of this problem is how to design an algorithm to estimate the real system state without distinguishing the real measurements from the measurements of malicious agents.In order to solve this problem,an adaptive distributed maximum a posteriori probability estimation algorithm is designed.As long as the number of compromised measurement streams is lower than a particular bound,all of the agents’local estimates,including malicious agents’local estimates,can converge to the true system state.Firstly,a centralized maximum a posteriori(MAP)estimation method is proposed based on Kalman filter.Combining a centralized MAP estimation with distributed consensus protocol,a distributed MAP estimation method is derived.Then,considering the measurement attack and analyzing the consistency of distributed estimates,a resilient distributed MAP estimation method is designed by exploiting the saturating adaptive gain,which gives a small gain if the deviation from the practical measurement resulting from the attacks is too large.At last,Numerical simulations are provided to evaluate the effectiveness of the proposed algorithm against measurement attacks.